In this Technical Tuesday installment,Heat Treat Today founder and publisher Doug Glenn sat down with Steelhead Technologies CEO Jeff Halonen to discuss the company’s recent acquisitions of Visual Shop and Bluestreak. Learn what the acquisitions mean for current users, including support and migration timelines, and how Steelhead plans to bring decades of heat treat-specific expertise into its ERP platform.
Heat Treat Today founder and publisher Doug Glenn spoke with Steelhead Technologies CEO Jeffrey Halonen about the acquisitions and what commercial heat treaters can expect in the coming years.
The Companies and Their Software
Steelhead Technologies provides an all-in-one AI-powered ERP platform designed for job shops and manufacturing operations, connecting production management with quality, specification compliance and tracking, scheduling, accounting, CRM, reporting, and other business functions.
Halonen called Visual Shop one of the first commercially available systems built specifically for the heat treat market. Visual Shop has served process-oriented job shops for more than 30 years. Approximately 150 enterprises currently use Visual Shop, about half of which are commercial heat treaters. The ERP system was developed for metal finishing, heat treating, coatings, and related industries, with heat treat functionality including furnace-load tracking, specifications and certifications, process temperatures and times, order tracking, shipping, and invoicing.
Bluestreak has similarly deep roots in the industry. The QMS and MES platform was initially developed for heat treating and focuses on production workflow, integrated quality management, traceability, and compliance documentation. It has approximately 90 clients, about 60% of which are commercial heat treaters. “Bluestreak is a QMS and MES,” Halonen said. “They’re more specialized on the heat treating.”
The Acquisition Timeline
Although Steelhead acquired Visual Shop and Bluestreak within weeks of each other, Halonen said the deals developed separately, with different circumstances behind them and their timing largely coincidental. With Visual Shop, Halonen said the age and architecture of the software meant significant development would eventually require rebuilding the product. “It was just at a juncture where there’s either a tremendous amount of investment — many, many millions of dollars — to start from scratch, essentially, on a brand new product, or we could provide a structured transition over to a more modern system where there’s a definitive path,” he said.
Bluestreak, by comparison, was growing and had strong client retention. Retirement considerations for Bluestreak president and founder Todd Wenzel also played a role. Bluestreak also brings years of heat treat-specific software development, particularly around specifications, certifications, furnace compliance, and other quality requirements. “Bluestreak is on its fourth generation,” Halonen commented. “It’s been working with heat treaters for many, many years.”
Halonen acknowledged that the experience of these two software leaders in the space is important as Steelhead expands its presence in the heat treat industry. “Especially with commercial heat treating, there’s a tremendous amount of nuance,” he said. “We need to approach this with humility.”
Next Steps for Visual Shop and Bluestreak Clients
For existing clients, neither system is disappearing immediately. Visual Shop and Bluestreak are now under the Steelhead umbrella, but their existing support teams remain in place. “For Visual Shop, right now nothing changes,” Halonen said. “You have the same product, call the same exact team.”
The longer-term picture is different. Steelhead is targeting the end of 2027 for sunsetting support for Visual Shop. Many Visual Shop users have perpetual licenses, and Halonen emphasized that retiring support does not mean those users will suddenly lose access to the software. The primary change will involve continued support.
Bluestreak has a longer runway, with its timeline extending into 2028. Its existing support team also remains in place. At the same time, Steelhead intends to bring heat treat-specific functionality developed within Bluestreak into its own platform. “We’re working very closely as a single team to provide all the features that Bluestreak has developed over the years that are great for heat treaters and pulling those into Steelhead,” Halonen said.
One point is clear for users of both products: future software development will be focused on Steelhead’s technology. “All proactive R&D investment in technology is going into the Steelhead platform,” Halonen said.
Cost Considerations
For Visual Shop users considering a transition to Steelhead, price will be an important part of the decision. Halonen acknowledged that Steelhead carries a substantially higher software price.
Steelhead argues that shops should compare total operating costs rather than software prices alone, including separate accounting or business software and employee time associated with order entry, invoicing, and other administrative processes. “What we’ve found, though, is that if you look at the cost of operating your business on Visual Shop — not just the dollars you pay for Visual Shop — you actually see a cost reduction for Steelhead,” Halonen said. Whether that calculation holds true will depend on the individual operation. Heat treaters will need to weigh software costs against staffing, current processes, ancillary systems, desired functionality, and implementation requirements.
For Bluestreak users, Halonen said the pricing gap between their current system and Steelhead is smaller, although a difference remains.
Migration and Support
The acquisitions could simplify one challenge for clients who choose to migrate to Steelhead: transferring existing data. Steelhead now has access to the source code and database structures of the acquired platforms, as well as the employees familiar with them. “The data you have in Visual Shop and the data you have in Bluestreak can be mapped over in a much more efficient, and much more granular, manner than otherwise would be,” Halonen said.
The Visual Shop and Bluestreak teams — approximately eight and nine people, respectively — remain in place to support existing products and assist with integrations or migrations. Together with Steelhead’s roughly 310 existing clients, the acquisitions bring the company’s total client base to approximately 550.
Halonen said Steelhead also intends to control the pace of implementations rather than taking on migrations faster than its teams can support them. “If we’re in a position where we’re not 100% confident that it’s going to be successful for our client, we’re going to use the calendar to our advantage and make sure we’re not overscheduling or overburdening,” he said.
Where Development Goes from Here
Steelhead’s long-term strategy is to combine heat treat-specific knowledge from the acquired companies with its broader ERP platform. For Halonen, that starts with the fundamentals heat treaters rely on every day. “How can I do furnace calibrations? How can I create this certification? What’s my operator’s experience going to be?” he said, describing the questions heat treaters bring to Steelhead. “They’re absolutely correct to focus on that.”
Steelhead also plans continued development of automation and AI tools, including its existing AI-assisted order entry and analytics. But Halonen said those capabilities depend on getting the underlying systems right first. “None of that works unless you have a rock solid foundation,” he said.
What Heat Treaters Should Know Going Forward
The acquisitions do not require Visual Shop or Bluestreak users to make an immediate software change, but they do establish a clearer direction for both platforms. Visual Shop is targeted for end of life at the end of 2027, while Bluestreak’s timeline extends into 2028. Existing support remains in place, while future R&D is being directed toward Steelhead.
For affected heat treaters, that creates a window to evaluate their options. Which heat treat-specific capabilities are essential? What other systems or manual processes operate alongside the current software? What data must be preserved? What will implementation require from employees? And what will each option cost when software and business processes are considered together?
Three platforms are now under one company, and the software landscape serving heat treaters has shifted. The next couple of years give shops time to determine what that shift should mean for their own operations.
This article was written by Heat Treat Today’s Editorial Team.
Heat Treat Today launches a new contributing experts series on emerging technologies reshaping heat treat operations. In this first installment, Zach Menard, CEO and co-founder of HRC Labs, demystifies deep neural networks (DNNs)— a technology quietly used in industrial settings for years — and explains how they turn accumulated furnace cycles, recipes, and quality records into predictive models that help heat treaters develop new recipes, minimize distortion, and cut cycle times.
This informative piece was first released in Heat Treat Today’sAugust 2026 Annual Automotive Heat Treating print edition.
Large language models (LLMs) have dominated recent conversations around artificial intelligence, but they are only one application of deep neural networks (DNNs). Years before DNNs were applied to human language, they had been deployed in industrial settings to optimize processes, improve yield, and support quality control.
These same, well-established approaches can be applied to heat treatment by learning relationships between process inputs — such as temperature, time, atmosphere, and alloy composition — and resultant metallurgical properties like hardness, distortion, or case depth.
It’s the first word in the acronym, DNN, that makes this learning possible. The neural network is said to be “deep” because of its many successive layers of neurons. Each neuron on its own is very simple. All it can do effectively is multiply and add together the numbers it has received from earlier neurons. However, when this process is repeated across many layers, the overall network is able to learn complex relationships that influence the heat treatment process.
By training on historical production data, DNNs can transform years of accumulated process knowledge into predictive models. Patterns that may otherwise remain buried across thousands of furnace cycles, recipes, and quality records can be captured quantitatively by the model itself.
In this way, operational experience and historical records become structured, reusable intelligence that can aid decision-making. Examples include when heat treaters are developing a new recipe, considering how to minimize distortion, or lowering cycle times.
As heat treatment operations continue accumulating larger volumes of process data, applying DNNs to this data offers a clear path toward AI-assisted process optimization, operational intelligence, and more adaptive heat treatment systems.
About the Author
Zachary Menard CEO and Co-Founder HRC Labs
Zachary (Zach) Menard is the CEO and co-founder of HRC Labs, an industrial AI company focused on process optimization and decision support for heat treatment. He holds a B.S. in physics from Vanderbilt University with a background in computational physics and machine learning research.
Machine learning is poised to become a powerful tool for modern heat treatment, transforming decades of production data into actionable process intelligence. In this Technical Tuesday installment, Zachary C. Menard, CEO and co-founder of HRC Labs, draws on real-world case studies in carburizing and tempering to explore how predictive models can accelerate recipe development, optimize operations, and support more consistent decision-making. Rather than replacing metallurgical expertise, data-driven tools offer a pathway to continuous improvement, greater efficiency, and stronger competitive advantage.
This informative piece was first released inHeat Treat Today’sJuly 2026 Annual Super Brands Issue print edition.
Introduction
Heat treatment is one of the most operationally demanding and variable processes in modern manufacturing. It operates at the critical intersection of process design, material variability, and the strict performance requirements of industries as diverse as automotive, aerospace, and construction.
As a result, even highly experienced heat treaters must routinely make high-stakes decisions: how to converge on a new recipe without expensive trial-and-error; how to increase throughput without sacrificing quality; and whether a deviation warrants rework or scrap.
Heat treaters are uniquely positioned to benefit from advances in machine learning when confronting these decisions. Years of archived process data, hardness measurements, and quality records represent a largely untapped resource.
When systematically modeled, this data can power predictive models that map recipe parameters to metallurgical outcomes.
This approach is not without precedent. Similarly complex industrial processes — aggregates mining, cement manufacturing, and even steel casting — have increasingly adopted machine learning to reduce variability, optimize cycles, and proactively detect process drift. These gains have not replaced domain expertise; they have amplified it.
The following sections outline how machine learning, when thoughtfully integrated with metallurgical understanding and domain expertise, can serve as an enabling infrastructure for modern heat treatment.
What is Machine Learning?
Machine learning (ML) refers to computer algorithms designed to learn how a system behaves by leveraging that system’s data. Among the most relevant ML models for heat treatment are multilayer perceptrons (MLPs), which are designed to learn complex, nonlinear relationships between process inputs and observed outcomes.
Image Credit: HRC Labs
In heat treatment, outcomes such as surface hardness, effective case depth, distortion, or residual stress do not depend on any single variable. They emerge from the simultaneous interactions between variables like temperature, time, atmosphere composition, material composition, part geometry, and quench severity and uniformity, to name a few.
MLPs are well suited to modeling precisely this type of behavior. Rather than assuming a predefined relationship or physical law, they learn the mapping from process parameters to measured outcomes directly from historical production data. Layer by layer, the model captures increasingly subtle interactions among variables, enabling it to represent effects that are difficult to express with predetermined equations.
Importantly, once trained on sufficiently representative data, MLPs can:
Predict part properties for proposed recipes in milliseconds.
Evaluate hundreds of candidate parameter combinations rapidly.
Provide explicit uncertainty estimates alongside predictions.
Be tuned to individual furnaces, load configurations, or heat treatment techniques.
While MLPs are well suited to modeling the complexity of the heat treatment process, another class of machine learning models called decision trees are particularly effective for structured operational decisions. Heat treatment routinely requires categorical judgments, from rework decisions to detecting process drift to root cause analysis.
Decision trees can categorize heat treatment runs based on how they differ from historical runs. For example, the tree may organize a run based on its temperature deviations, fluctuations in carbon potential, or variation in effective case depth from the average. Each terminal node or “leaf” of the tree represents a cluster of historically similar runs.
Because these clusters are associated with prior corrective actions and results, the tree can provide probabilistic guidance grounded in historical precedent. For example, when analyzing a recent batch of parts that missed their target case depth, the tree might find that among comparable historical runs, 80% were successfully re-processed under a modified cycle, while 20% were scrapped. This enables faster root cause analysis and supports decision making.
Heat Treatment Applications
ML is particularly well suited to modeling and managing the many interacting variables that determine heat treatment outcomes. These capabilities translate into three practical applications: recipe development, process optimization, and auxiliary decision support.
Recipe Development
Heat treaters rely on operational experience, test coupons, and simulation tools to inform recipe design. However, new alloys, part geometries, or throughput demands often require iterative trial runs. Each additional load dedicated to experimentation represents furnace time removed from production.
Finite element simulations can narrow candidate recipes, but their accuracy depends on material properties, boundary conditions, and calibration to production conditions.
ML models such as MLPs offer a complementary approach to physical simulations. Trained on historical production data, they learn how a specific furnace, material, and process behave under real production conditions. Once trained, these models can rapidly evaluate candidate recipes against hardness, case depth, and distortion targets.
Importantly, predictions can be accompanied by uncertainty estimates, allowing heat treaters to assess risk alongside expected performance. By reducing the number of physical trial runs required to converge on a robust recipe, ML can accelerate the path from testing to production while preserving metallurgical oversight.
Process Optimization
Once a recipe has been demonstrated to meet specification, the focus shifts from feasibility to efficiency and robustness. The objective becomes reducing cycle time and energy consumption while maintaining process stability and required part properties.
In practice, optimization often proceeds through incremental parameter adjustments informed by experience. Soak times may be shortened or quench conditions modified in pursuit of efficiency gains. Yet the interdependence of heat treatment parameters makes it difficult to predict how such changes will affect process robustness.
ML models provide a structured framework for navigating this landscape. Once trained, they can:
Evaluate a multitude of potential changes to a recipe.
Estimate the impact of efficiency-driven adjustments on final properties.
Quantify uncertainty, identifying parameter sets that are not only efficient but stable.
Rather than relying solely on sequential physical trials, heat treaters can assess proposed modifications computationally before allocating furnace capacity.
Each additional production run further refines the model. As data accumulate across varying materials, geometries, and operating conditions, predictive fidelity improves. Over time, optimization transitions from periodic study to continuous, data-driven improvement.
Auxiliary Decision Support
Recipe design and optimization are only part of the operational challenge in heat treatment. A substantial portion of heat treaters’ time is devoted to diagnosing deviations, determining appropriate corrective action, and monitoring for process drift.
When a batch fails to meet specification, multiple correlated factors may plausibly explain the deviation. As a result, root cause analysis often requires time-consuming review of process logs and historical records.
Image Credit: HRC Labs
Decision-tree-based models provide a structured method for accelerating this analysis. By learning from historical runs and their documented outcomes, the model can classify a new deviation according to its similarity to past cases. For example, the model may determine that comparable historical runs were most frequently associated with temperature non-uniformity rather than alternative causes.
This approach offers three practical advantages:
Image Credit: HRC Labs
Faster root cause analysis and reduced diagnostic downtime.
More consistent, data-backed rework decisions.
Earlier detection of process drift and emerging equipment issues.
Importantly, these models do not override heat treaters’ judgment. Rather, they surface structured historical context that may otherwise remain fragmented across logs and institutional memory. Over time, this improves consistency, auditability, and knowledge transfer.
Machine Learning in Heat Treatment
The preceding sections have outlined several proposed applications of ML in heat treatment. The following sections examine documented cases of successful ML application in heat treatment.
Case Study 1: Carburizing
Jia et al. (2023) demonstrated the use of multilayer perceptrons to model the vacuum carburizing process for a low-alloy carbon steel cylinder. Using primarily simulation-derived data supplemented by limited experimental runs, the authors trained an MLP to predict case depth, surface carbon concentration, and surface hardness from process parameters including carburizing and diffusion temperatures, cycle durations, and carbon concentration levels.
Image Credit: HRC Labs
Once trained, the model rapidly identified parameter combinations that satisfied specified hardness and case depth targets while reducing computational cost relative to traditional simulation tools.
Zhang et al. (2025) took a similar approach but focused on atmospheric carburizing. The study’s goal was to train an MLP to predict post-carburizing carbon concentration across multiple part geometries for SAE AISI 3316 gear steel. Representative test coupons were used to validate the simulation-derived training data against production runs.
The model predicted post-carburizing carbon concentration with average errors near 2%. While limited in scope, both studies demonstrate that ML can replicate complex diffusion-driven behavior and accelerate recipe evaluation. These investigations focused primarily on carbon concentration and hardness prediction; the same modeling frameworks could be extended to distortion and residual stress prediction.
Case Study 2: Tempering
To further explore the performance of ML in heat treatment, publicly available tempering data from the classic study of Hollomon and Jaffe (1945) were analyzed. Their model combines temperature and time into a single tempering severity parameter, M. While effective within specific steels, prediction errors increased substantially as carbon contents varied. This model failed in particular to extrapolate to the behavior of lower carbon steels, with errors surpassing 10 HRC in some cases.
Image Credit: HRC Labs
To address this limitation, ML was used as a corrective layer on top of the traditional Hollomon-Jaffe model. The Hollomon-Jaffe equation provided baseline hardness predictions, while the ML model learned systematic corrections based on alloy composition and processing conditions. This approach reduced prediction error dramatically — from over 10 HRC in some cases to 2 HRC — while preserving the underlying metallurgical framework.
A common concern with ML models is that they behave like “black boxes.” However, modern interpretability tools allow engineers to examine how models arrive at their predictions. In this study, SHAP (SHapley Additive exPlanations) analysis was used to examine how the model corrected the Hollomon-Jaffe prediction. This revealed clear trends in how quantities like carbon concentration informed the model’s corrections. Rather than behaving like a black box, the model learned consistent metallurgical patterns in the data.
This case study illustrates how ML models can improve traditional approaches when they are limited by assumptions about physical parameters or cannot accommodate certain inputs. Unique furnace behavior, fixturing configurations, and part processing history are difficult to capture and calibrate using traditional simulation tools. Additional work should explore the integration of ML as a corrective layer for simulation software.
Strategic Implications and Competitive Advantage
The case studies presented here illustrate a broader principle: ML is most powerful in heat treatment not as a replacement for metallurgical understanding, but as a mechanism for formalizing and extending it.
When applied thoughtfully, ML can accelerate recipe development, reduce trial-and-error, and support structured diagnostics grounded in historical precedent. Unlike static optimization studies, trained models improve continuously as new production data become available. Over time, they become increasingly plant-specific, capturing nuances in furnace behavior, fixturing, material sourcing, and operating conditions that are difficult to encode with predefined equations.
Successful implementation will require high-quality process data and close collaboration between heat treaters and data scientists. ML is not a substitute for metallurgical expertise, but a tool for extending it.
The opportunity is substantial. Heat treatment has generated decades of process data that remain underutilized. Shops that convert this historical record into predictive intelligence will gain measurable advantages in efficiency, robustness, and process consistency. As competitive pressures increase and tolerances tighten, the ability to continuously learn from operations may become a defining differentiator.
Heat treatment has a rich history of innovation and experimentation. The integration of ML represents the next step in that progression.
Acknowledgements: The author would like to thank Dan Herring, The Heat Treat Doctor®, whose insight and guidance were instrumental in refining the direction of this work.
References
Hollomon, J. H., and L. D. Jaffe. 1945. “Time-Temperature Relations in Tempering Steel.” Transactions of the American Institute of Mining and Metallurgical Engineers 162: 223–49.
Jia, Hongliang, Dong Ju, and Jian Cao. 2023. “Machine Learning Based Optimization Method for Vacuum Carburizing Process and Its Application.” Journal of Materials Informatics 3: 9. https://doi.org/10.20517/jmi.2022.43.
Zhang, Y., Z. Tang, Q. Yin, et al. 2025. “Machine Learning Based Prediction of Carbon Concentration in Carburized Steel.” Scientific Reports 15: 33678. https://doi.org/10.1038/s41598-025-18531-8.
About The Author:
Zachary Menard CEO and Co-Founder HRC Labs
Zachary (Zach) Menard is the CEO and co-founder of HRC Labs, an industrial AI company focused on process optimization and decision support for heat treatment. He holds a B.S. in physics from Vanderbilt University with a background in computational physics and machine learning research.
In this episode of Heat TreatRadio, host Doug Glenn sits down with Jeff Halonen, CEO and co-founder of Steelhead Technologies, to discuss the company’s recent acquisitions of Visual Shop and Bluestreak. Halonen explains what brought the deals together, what the acquisitions mean for current users, and how Steelhead Technologies plans to integrate the platforms and their heat treat-specific capabilities. The conversation also explores the company’s broader investment in technology, AI, and the future of software for commercial heat treaters.
Below, you can watch the video, listen to the podcast by clicking on the audio play button, or read an edited transcript.
The following transcript has been edited for your reading enjoyment.
Introduction (00:05)
Doug Glenn: Well, welcome everyone to another episode of Heat TreatRadio. I’m your host, Doug Glenn, the founder and publisher of Heat TreatToday.
Today I have the great privilege of speaking with Steelhead Technologies CEO, Jeffrey Halonen. We’re going to be talking with Jeff about his company’s recent acquisition of two competitors in the commercial heat treating sector of our market.
At Heat TreatToday, our primary market is not commercial heat treaters. It’s an important sector, but it’s about 15% of our total circulation. Most of the rest of it is in the captive market. But today, we’re going to be talking about this commercial market, and it should be a special interest to anyone in that commercial heat treating market, especially if you are a current user of Bluestreak or Visual Shop, or of course, a Steelhead Technologies product.
First, can you tell us about yourself and Steelhead Technologies?
Host of Heat TreatRadio Doug Glenn (left) and CEO and co-founder of Steelhead Technologies Jeff Halonen (right)
Jeff Halonen: Thanks for having me, Doug. I appreciate the differentiation between captive and commercial heat treaters. For commercial heat treaters, their products can by anything that flies, goes into space, data centers, computers, etc. So, I think that commercial heat treaters find themselves in one of the most important seats in manufacturing because of the pace of iteration. If you think about it, if you’re doing brake rotors, and it’s the same brake rotor for 40 years, you’re going to bring that in-house. It makes sense to buy the oven.
But if you’re changing your designs every six months, which is what happens in aerospace and data centers, etc., then commercial is the clear path.
My name is Jeff Halonen. I’m a mechanical engineer. My career started at General Motors. I got into some heat treating, but it was super mild compared to what everyone gets into, as in body structures — there’s a lot of thermal management components to assembling a vehicle.
I’m one of the co-founders of Steelhead, and we are a technology partner for job shops. We serve primarily commercial heat treaters, but also metal finishers, platers, powder coaters, Nadcap, wet paint, thermal spray, and also some fabrication shops as well. Our customers may have as low as three people, but typically it’s 20 to 300 people, somewhere in that range where they’re big enough to have a lot of problems, and then they’re also a job shop. Parts are coming in, phone calls, high-volume, fast-paced. These aren’t $10,000 invoices, $20,000 invoices — they are smaller, a few hundred or thousand dollars, and there’s high volumes of them.
Everyone is dealing with the problems of labor turnover and other challenges. It’s a very nerve-wracking business. The revenue volatility is kind of extreme a lot of times, and your visibility and your control over that revenue volatility is not high. There’re ways you can diversify, but there’s challenges.
We have a core philosophy that job shops are actually ideally suited to leverage technology to the hilt to compensate for all of these challenges.
It’s almost like you’re in an environment where the facts change every hour, every day. This means that knowing what’s going on and making decisions quickly becomes even more important. It’s like the F-35 fighter jets that have those goggles with all the quick stats. Why? Because in a dog fight, the faster you make decisions and have data, the better off you are. Whereas if you’re canoeing across a lake, you don’t really need a lot of real-time KPIs.
We’re huge on technology, and we work with job shops all day long.
The Product (03:53)
Doug Glenn: Are you an ERP (enterprise resource planning) system or how would you classify your product?
Jeff Halonen: We’re all in one, to the extent we can be. That includes ERP, financials, native accounting, MES (manufacturing execution system), spec management, QMS (quality management system), quality management, CRM (customer relations management), timesheet, payroll integration, maintenance, purchasing, receiving, etc.
We have found that smaller businesses do not like to jump into 20 different software programs because you’re shoveling data all over the place. Even something like purchasing — how is that related to maintenance? Well, you had to replace a motor, and then you had to purchase a motor, then you had to receive it, but it went to one of four buildings. You don’t know where it’s at. The magic is in the connectivity of everything, and then if adding AI on top of that, you’re a fully connected business.
The Acquisition Timeline (04:42)
Doug Glenn: So, enterprise-wide, top to bottom.
Now the big news is this acquisition that just recently took place, and that’s what we want to talk about today. First off, let’s just get some facts about the acquisition. When did it happen?
Jeff Halonen: Yeah, so there’s a separate story and arc and strategy of how it made sense for all parties with both of them. As far as timing, Bluestreak was a couple weeks ago, and then Visual Shop was three or four weeks before that.
The timing is relatively coincidental — it just happened. The conversations just so happened to move in parallel. Timing-wise, there’s no explicit link between the two acquisitions, other than the fact that they both picked up steam around the same time as each other.
Doug Glenn: That’s right. Can you describe the companies involved?
Jeff Halonen: Visual Shop is a Cornerstone Systems product out of Illinois. They’ve been around — they’re essentially the pioneers in heat treating and metal finishing, as far as having a commercially off-the-shelf product available with support and service for heat treaters.
And then Bluestreak out of Wisconsin, which is really similar in terms of focus, but Bluestreak’s a much more modern system, cloud-based offering. Both companies have a huge disposition towards high service, which is something we’re really happy to see as we kind of bring the companies together.
Our number one core value is customer obsession, and we understand that it’s not just software. If your business is completely static, you have 10 employees, and it’s just the same business for 20 years; you don’t need a ton of service. But most of these shops are growing and adding capabilities, and that’s a common ethos across the two of them.
Visual Shop has about 150 enterprises on the software today, and Bluestreak has about 90. Half of Visual Shop customers are commercial heat treaters, and 60% of Bluestreak are commercial heat treaters.
Doug Glenn: The other percentages that are not commercial heat treaters are outside the heat treat industry. Still probably job shops, but outside the heat treat industry.
Jeff Halonen: Correct. Highly tangential. The same parts are going in and out of heat treaters to blasting shops, copper plating shops, etc. It’s typically metal finishing for the remainder.
The Acquisition Arc (07:35)
Doug Glenn: You mentioned earlier about the arc of each acquisition, the timeline, and the reasoning. Can you tell us about the arc of each of those acquisitions?
Visual Shop
Jeff Halonen: With Visual Shop — this product was launched around 1992. It hit a point where continuing to improve essentially requires a complete rebuild. The position of the business, and the financial position of both Steelhead and Visual Shop, is that it’s a very affordable product.
It was the lowest cost product available for job shops, which was great, but the result of that was that there were not a lot of resources to invest in building the product and accelerating development. So, it was just at a juncture where there’s either a tremendous amount of investment — many, many millions of dollars — to start from scratch, essentially, on a brand-new product, or do we provide as it kind of gets towards the end of life, a structured transition over to a more modern system where there’s a definitive path. There’s someone you can call. Obviously, there are many paths that every shop can take, but it provides a smooth transition.
Now the entire Visual Shop team is now part of team Steelhead. Nothing changes for Visual Shop customers — they still call Visual Shop. It’s the same product, the same service. But now there’s a path for migration there.
Bluestreak
On the Bluestreak side, the company was actually growing. Bluestreak is a great product. It has great retention numbers, approximately 97% gross dollar retention, which is a common metric in software — that’s exceptional. So Bluestreak’s clients are loyal. They love the product. Todd and the rest of the Bluestreak team have built an incredible product for heat treating.
The product captures a lot of the nuance around specifications, certifications, and oven compliance, like blocking and tackling. That’s one thing we’ve come to appreciate over the years — you can have flashy AI tools, but shops cannot focus on the upside, on the growth, unless the fundamentals of how they run their business are rock solid.
They need to feel that they’re on rock-solid concrete. If they cannot create a certification, if Battelle Helicopter calls us and asks for documentation of something and they are not confident it can be provided — if they are not confident in the accuracy of instruction provided to operators or the ability to process parts correctly — the company cannot even focus on the nice upside of technology.
Related Reading: Click on the image above to see how digital tools can streamline Nadcap certification management and reduce manual data entry for heat treat operations.
All that to say, the partnership with Bluestreak is a really exciting one for Steelhead to be able to build absolutely bulletproof heat treating technology. Todd (Wenzel) and our engineering team — the Steelhead engineering and product team — are in close contact, and there are things that Bluestreak has built that are on its fourth generation. They have been working with heat treaters for many, many years. So, we’re working very closely as a single team to provide all the features that Bluestreak has developed over the years that are great for heat treaters and pulling those into Steelhead.
Bluestreak is a QMS and MES, and then Steelhead is the broader platform, including ERP and finance. We also have an MES, but they’re more specialized on the heat treating. We are kind of integrating and combining features and products to get a great rock-solid foundation in place.
Like doctors say, first do no harm, and then press the gas on AI. I was just at a shop yesterday, a large heat treater. When I walked into the office, there were employees doing invoicing, and it looked like 1997. Hey, the machine works, right? There were some profitability challenges that they were working to address, but for other business functions, that’s where it gets really exciting.
Some may wonder if they can even look at AI. Everyone is in a margin-pressed environment. Everyone has costs. If you look at the overhead it takes to run your shop, and you can reduce that meaningfully by tens of percent, that goes straight to the bottom line. Now you can go buy those $3 million ovens that you love to buy, or you can walk to the trade show with a little more swagger because you got a little extra net margin at the end of the day to invest back in the business instead of dumping that into processing paperwork.
Related Reading: How could AI connect heat treat data, tools, and operators? Click on the image above to learn more about the emerging role of AI and Model Context Protocol in thermal processing.
We have a couple AI products out today, AI order entry and AI analytics, to get your team to where you can literally chat with your business — have an AI agent that’s like a CFO or a quality manager — so your team can spend time solving the problems instead of digging through charts.
The exciting part is that’s these are the first of 10–15 AI products on our roadmap. But none of that works unless you have a rock-solid foundation.
Doug Glenn: Need to have the foundation.
Jeff Halonen: That is the number one priority. What we found is if we sit here and talk about AI, that’s fine. But how can I do furnace calibrations? How can I create this certification? What’s my operator’s experience going to be? And you know what — they’re right. They’re absolutely correct to focus on that. We’re taking the order of operations very seriously, but it’s still very exciting.
Acquisition vs. Competition (12:44)
Doug Glenn: Two more questions on the acquisition itself. Why buy as opposed to just competing them out of the market?
Jeff Halonen: Yeah, it’s a good question. I’ll take them separately because it is different for both of them. With Visual Shop, the product works. The business operates. It’s fairly common for us to meet with shops that have more financial challenges that are running on that system. That’s like an invisible, parasitic tax. But what they do see is, a) it works, and b) it’s a very low cost. You’ll have businesses doing $20 million in revenue, spending $6,000.
I know our products are a little more premium offering, and we would like to bring the absolute best we can bring. Even globally, as far as what businesses pay for technology, Visual Shop is a very low price. There’re a couple reasons this acquisition makes sense.
For one, it’s actually good for the customers. Visual Shop’s customer base and revenue have been decreasing. They had about $1.2 million in revenue and about 150 customers, and those numbers have been decreasing for about four years.
It just gets to a point where there’s just no good options. And with those options, the timelines are really compressed.
So, in many ways, we’ve extended the timeline and increased the stability that’s available. There were also many mental hurdles with the Visual Shop side. We’ve had many people send us all-caps emails saying, “Thank you for buying Visual Shop.” It would be from the lieutenants — the quality manager, the production manager, the general manager — because they suffer. They are the system. It’s like their nights and weekends, and their stress levels — that is the system.
Sometimes you have a situation where the finance decision is, “Hey, this low-cost system will work, so therefore take no action.” But they’re not seeing all the costs that are kind of hidden there. So that’s where this acquisition makes a lot of sense. It provides a stable path, but also a bit of an impetus for folks to reevaluate next-generation or modern options.
Steelhead has attracted a bit of investment. We are growing quite aggressively. I would say we’re pushing the limits of what technology can do for job shops, and that’s resulted in a lot of exciting growth. But the thing I’ve been pushing with our team is humility. Especially with commercial heat treating, there’s a tremendous amount of nuance. The vocabulary alone will humble you if you haven’t been in that environment for a while.
The messaging to our team was, “Hey, we need to approach this with humility.” That’s where the Bluestreak partnership is exciting, because it’s different from Visual Shop in the sense that Bluestreak was growing and in a good spot profitability-wise.
Todd was looking at retirement options as well — there were some time dimensions there. This acquisition wasn’t as acute as Visual Shop, as far as continuity plans for customers and creating predictability.
The reason why the Bluestreak deal made sense is because we believe heat treaters are a critical portion of the manufacturing ecosystem. There are all these oil tankers trying to get out to the open sea, all the fabrication shops, the space launches. I think they’re going from 110 space launches a year to 220. They’re literally doubling space launches from 2026 to 2027. And then you have all these startups, and that’s great. You have 500 oil tankers here, but now it’s got to get through this. That’s the role of commercial heat treating. It’s others too — anodizing, plating, and others — but heat treating is certainly a part of that constriction if you look at the manufacturing picture.
That’s why it made sense to combine Bluestreak’s rock solid understanding of how you run a heat treating shop reliably. This is how you pass your Nadcap audits every time. Then incorporate the larger business platform to try and boost the margin and growth opportunity and get on offense.
The Bluestreak product progress has been slowing over the last several years. We believe there’s an opportunity to step on the gas in terms of what technology can deliver. That’s where that partnership makes sense.
It puts us in a position to have a bulletproof offering. Here’s the source code for Bluestreak, here’s the source code for Steelhead, and here’s the developers that made both of them work together to make sure we’re putting the absolute best product in the hands of heat treaters.
The Financial Investment (18:17)
Doug Glenn: One last question about just the acquisition itself. Can you talk about the financial aspect of the acquisition?
Jeff Halonen: I’m one of six co-founders of the company. We bootstrapped it for a year. I’m an ex-automotive engineer from Michigan. We did get to a point where we realized that if we want to grow, we may have to pursue financial backing, and we did. We pursued venture capital.
For the first four years, we were venture-backed. Venture is a very aggressive type of investor. There’s a lot to read about there. They do serve a great role in society. There are not a lot of people that would give $2.5 million to a guy with a PDF who’s hollering at a webcam saying, “We’re going to go do this thing.” They have some downsides, too.
Last November, we transitioned to having four venture capitalists as part of the company. We’re down to one now. Then a growth equity firm stepped in called Mainsail. They have around 30 ERPs in their portfolio. This is what they do all day long. It was an $84 million investment that they made, and part of that was relieving the venture capitalists of their seat at the table. Part of that investment was operating capital to invest in growth. It’s right in the name, growth capital.
I’ve had shops ask me what their intentions are as investors. That’s a rock-solid question. Growth equity is a variant of private equity. They are looking for a financial outcome five years out on the horizon. However, ask yourself what leads to that outcome. This is capitalism, and just like every commercial heat treater in the world — if you provide a great product to your customer on time, you have a financial outcome.
A world where there’s a misalignment of incentives, specifically, almost doesn’t exist. If Mainsail is looking for a return on their investment five years from now, the next financial sponsor, whoever it is, will be looking at one number first and foremost, and that’s retention — gross retention.
We’re an ERP. We have one-year contracts with our customers. That’s a very modest contract length in the ERP world. That means our customers can choose a different path every single year. If we aren’t retaining customers, then we’re also not the cheapest. Every year customers have to say, “It’s still worth it.” That puts an immense onus on us to deliver.
So, the only path for us is to succeed and to provide a successful outcome for the financial sponsors — who, by the way, are financing a ton of development R&D, many millions of dollars of development going into heat treating.
There’s a lot of positivity here for everyone. There’s a ton of alignment here, and retention of customers is number one, and the only way we can retain customers is to provide unbelievable value year after year.
If we aren’t improving their margins and we aren’t helping them grow, then it’s going to be tough sledding for us. But that’s what we look towards every day. That’s why customer obsession is our number one core value. It’s really the only path forward.
The Clients (21:45)
Doug Glenn: I did want to talk about customers too, because I’ve heard a couple of different things just in talking with some people in the industry about the recent activity here. Will Cornerstone customers and Bluestreak customers be impacted essentially the same?
I’ve heard that Cornerstone customers are essentially going to have to abandon their system, more or less, but Bluestreak customers will be able to maintain their system. Can you tell us how it’s going to impact each of those two different clienteles?
Jeff Halonen: There’s some aspects that are the same for both. The first is that within the product portfolio now, we have Steelhead, Bluestreak, and Visual Shop, all under the Steelhead umbrella. All proactive R&D investment in technology is going into the Steelhead platform. We’ve already invested millions of dollars into heat treat technology in the Steelhead platform.
The CMMC Phase II rollout has been paused, but cybersecurity requirements remain. Click the link above to learn more about what the latest developments mean for captive and commercial heat treaters.
The architecture is very modern and advanced. The platform will actually have a FedRAMP-moderate-equivalent version available early next year. We’re targeting January of next year, which means if you’re CMMC, it’s extremely easy just to switch to the FedRAMP cloud product, and the burden of becoming CMMC Level 2 goes away.
Out of the three products, all new R&D, all the AI products, will be invested into Steelhead. However, we also want to make sure there’s business continuity for all the shops using the Visual Shop and Bluestreak product. All Visual Shop and Bluestreak customers are Steelhead customers. Customer success is number one priority. For Visual Shop, right now nothing has changed. You have the same product, call the same exact team. There’s no change at all.
However, we’re targeting the end of 2027 for the end of life on Visual Shop. Many Visual Shop customers have perpetual licenses, meaning they’ve been using it for free, essentially unsupported, into perpetuity. There are quite a few shops that have been doing that already for a long time. There are a few that are on subscription, but the vast majority are perpetual, which means its support. It’s the ability to pick up the phone and make a call, not like, “I have access to software,” and the software just went away the next day.
We are targeting the end of 2027 for Visual Shop. For Bluestreak, that timeline is extended beyond that. But the same — all new R&D is going into the Steelhead product, and the functionality that customers know and love inside of Bluestreak is being invested into the Bluestreak product, and that timeline’s out into 2028 for end of life on that one.
The Cost (24:29)
Doug Glenn: Is there a substantial cost differential, I assume, between what you guys will be charging Cornerstone customers and what they are/were paying?
Jeff Halonen: I’m happy to address this question. I just can’t stress enough — if you go out there and just say, “I’m not going to buy Steelhead, I’m going to go buy some other software,” you will not find another Visual Shop price. Ultimately, that did contribute to the outcome. As such, there will be a significant jump in price on the Visual Shop side.
What we’ve found, though, is that if you look at the cost of operating your business on Visual Shop — not just the dollars you pay for Visual Shop — you actually see a cost reduction for Steelhead. Steelhead is actually cheaper than running Visual Shop almost every time. But it requires you to consider counting the cost of the three employees doing order entry and the two employees doing invoicing. You have to consider the real cost of running Visual Shop. You have to count the whiteboard, the walking around. There’s the real price, and it’s a pretty big price jump.
Also, you benefit from the technology and the AI tools. We just released two AI tools in the last couple of months. Ultimately, it is a shift of saying, “Hey, I believe in technology, I believe technology can help drive my business,” versus saying, “I just need a system to do packing slips and invoices, and it should be as close to $0 as possible, and I don’t care about it getting better, and I don’t think AI applies to my business.”
That’s the mentality of some shops and that’s fine. But the reality is that it’s impossible for us economically to provide the same level of service. We’re on site all the time. I was on site yesterday. We’re on airplanes all the time. I’m out of Detroit, Minnesota, Texas, California, Pennsylvania, etc.
It’s not just a bunch of software, and you figure it out. It’s software, plus help getting it launched and implemented. When we have a new tool, we can provide that help. It’s very much a symbiotic relationship.
We specialize in the ERP and technology on the change management side. Our customers specialize in their business, and each business is different, so they’re teaching us, “Hey, this is how our business works; this is the requirements we have.” And we’re saying, “Okay, this is how we can use our technology to achieve that outcome.”
For Bluestreak, it’s similar. The gap is much smaller though. Bluestreak is directionally towards Steelhead pricing, but there’s still a gap there from Bluestreak to Steelhead. There are some efficiencies to be gained to close that gap, but it’s more about really leaning into leveraging technology for your business.
Companies also have to consider what they are paying for accounting software. What are you using for ERP? How are you running all these other ancillary systems surrounding your MES, QMS? It’s essentially an expansion of the footprint as well, which offsets a lot of the cost. There’s efficiency gain there, then there’s also access to the entire AI investment and much more modern product.
The Impact (27:48)
Doug Glenn: I have two more questions. We talked about the impact on customers. What is the impact to Steelhead Technologies? You’ve substantially increased the number of customers, and therefore the need for customer support. What type of changes are being made inside of Steelhead to meet that — the number of phone calls coming in, the number of emails coming in, and other challenges?
Jeff Halonen: Steelhead has around 310 customers, Visual Shop has around 150 customers, and then Bluestreak has around 90. In total, around 550 customers. In 2025, we successfully deployed around 95 accounts, meaning completed the handoff. They’re in support, steady state. There’s obviously always continuous improvement, but the surgery is done. They’re running completely flat out. The growth has continued to accelerate since then. I don’t know what the number is going to be this year, but I wouldn’t be surprised if it got closer to double that.
We have around 120 folks on the Steelhead team, and the Visual Shop and Bluestreak support is unchanged. It’s still there. There’s roughly eight and nine folks on the Visual Shop and Bluestreak teams, respectively — not only supporting the continued use of Visual Shop and Bluestreak products, but then during any integration, or if you do choose to essentially upgrade to Steelhead product, you now have an opportunity where both vendors are same vendor. Like with migrating data, and other aspects like that.
Steelhead’s investing a tremendous amount in automation because we have access to database structure on both sides. We have access to source code on both sides. We have access to engineering on both sides. So, the data you have in Visual Shop, the data you have in Bluestreak, can be mapped over at a much more efficient and granular manner than otherwise would be.
We are also growing our team. So, shoot us a note. We have a career page on our website.
We have a fairly substantially staffed team, and there’s some healthy growth going on there. If we’re in a position where we’re not 100% confident that it’s going to be successful for our customer, we’re going to use the calendar to our advantage and make sure we’re not overscheduling or overburdening, to a point that’s detrimental to the customer.
When we look at a customer or a partnership, 10 to 15 years is our ideal case scenario. Rushing to start a project when we’re not ready to capitalize on it, to save a couple of months, a couple of weeks or something like that, is just not smart. It’s very shortsighted, and we seek to avoid that.
The Message (31:00)
Doug Glenn: One last question for you. One of the advantages of doing this interview is that you can send out a main message. What message would you like to communicate to current customers of Steelhead, of course, and customers of Cornerstone and Bluestreak?
Secondly, what message do you want to give to the untapped market — the companies that you don’t have right now that you’d like to reach?
Jeff Halonen: We don’t serve injection-mold job shops; we don’t serve machining job shops. There’s a lot of job shops we don’t serve, because we understand that the nuance and the depth and detail matters. So, serving heat treating, as an example, was not just like whim, right? It’s a very intentional move, but the message is very simple: investment. Steelhead has historically invested a ton into technology for job shops, and these acquisitions represent another very material investment.
They would only make sense if you planned to continue investing. As you pay attention to the news and you see AI in every other headline, you may wonder what you are going to do about AI. We use AI a lot internally, and what we found is if you just go buy a bunch of random software and try to patch AI on top of everything, it’s okay for one-off little errands. Everyone can do that. You’re going to get five or ten percent more efficient by using AI in a one-off, à la carte method.
But to truly leverage AI — which equates to a new material that’s 90% cheaper or 40 times stronger — you have to bake it into the bones of your business, into the operating system of your business.
That’s what we’re really excited to continue investing in. One fear that job shops might have is about leveraging AI. There’s this new superpower that’s out there, and it’s tough just to be on your own and bolt it all together yourself.
It’s actually quite complex to leverage it to its maximum. What we’re looking to provide to job shops is a turnkey product and service. Someone that literally comes on site, helps your operators adopt the system, configured just to your industry, and then bolts a ton of AI on top of it. It’s got your finance, your inventory, your certifications, your operator training, your scheduling — everything in one and then bolts AI on top of it.
Our mission is that job shops in the United States of America have technology that puts them actually at the front. We want these job shops to be some of the most profitable manufacturers. If you’re a job shop, go look at your biggest customer, with 12,000 employees, and 2,000 employees. Good luck. They will change, but they’re going to move so slowly. Our vision is that you wake up some day and look around and think, “I have the best setup on the planet. I’m actually way ahead of all my peers in terms of larger technology.”
We want to make that super easy, so we view ourselves as a technology partner on that, and that’s what we’re focusing on. A key takeaway is investment in technology, and this is emblematic of us doubling, tripling, quadrupling down on investing and being the absolute best technology partner we can and the same for our customers. If you’re an existing Steelhead customer, same thing. We have our foot on the gas, and we’re really excited to be the best technology partner we can be.
Doug Glenn: Super. Alright, Jeff, thank you very much. Appreciate your time. Anything we can do to be helpful to you guys, let us know.
Jeff Halonen: Excellent. Thank you, Doug. And thank you for everything that you guys do as well.
About the Guest
Jeff Halonen CEO and Co-Founder Steelhead Technologies
Jeff Halonen is the CEO and co-founder of Steelhead Technologies. Before Steelhead, Jeff worked as a mechanical engineer at General Motors, gaining experience with complex manufacturing systems and large-scale production environments. Throughout his career, Jeff has walked countless job shop floors around the country, listening firsthand to owners, managers, and production teams as they work to solve bottlenecks, improve profitability, and achieve sustainable growth.
This exposure has shaped the vision for Steelhead: to provide practical, modern tools that streamline operations, reduce overhead, and help shops get ahead. Jeff pushes an aggressive AI roadmap to enable shops to double revenue without adding a single front-office headcount. His conviction is that the right technology partner can make job shops some of the most profitable manufacturers in the country.
Jeff lives in southeast Michigan with his wife and four sons. Outside of work, he hunts, fishes, and frequents the local farmers market.
Heat TreatRadio host, Doug Glenn, sits down with Peter Sherwin, director of Strategic Marketing at Watlow, to discuss how the next generation of process control technology is being shaped by the evolving needs of the heat treating industry. Their conversation explores the role of AI, cybersecurity, workforce development, energy efficiency, and compliance in modern furnace operations, as well as how Watlow’s Edge Process Management (EPM) platform aims to bring these capabilities together in a unified system. Looking beyond a single product launch, the episode examines the trends that could define heat treating over the next two decades.
Below, you can watch the video, listen to the podcast by clicking on the audio play button, or read an edited transcript.
The following transcript has been edited for your reading enjoyment.
Introduction (1:04)
Doug Glenn: Welcome to another episode of Heat TreatRadio. I have the pleasure of actually being face-to-face here with our guest today, Peter Sherwin. Peter is the director of strategic marketing at Watlow, and we’re going to be discussing an exciting product that Watlow recently launched.
Host of Heat TreatRadio Doug Glenn (left) and Director of Strategic Marketing at Watlow Peter Sherwin (right)
There’s been some discussion in the industry about Watlow’s commitment to the thermal processing market. Can you address that?
Peter Sherwin: One of Watlow’s taglines is “wherever thermal is critical.” Obviously, thermal is very critical in heat treatment. My background is from Eurotherm, now combined with Watlow. I’ve been with the organization for around 17 years now. There was always a bias toward heat treatment within Eurotherm, but Watlow has a much broader portfolio.
Heat treatment is still very important to Watlow, but we play in a number of different spaces. The business is split into two key verticals: semiconductor and industrials. The heat treatment activity resides under the industrials banner, whether it’s automotive, aerospace, etc. So, it’s still very key to us, and I think we’ve been pushing more through our distribution channel as of late, particularly in the last twelve months.
We have been working on training and re-skilling our workforce, as well as developing the next generation of products. That’s been taking quite a bit of our focus, but we definitely have not stepped away from heat treatment.
Doug Glenn: Watlow has been dedicated to providing the resources to do that. You have been with the company when Eurotherm was standalone, when Eurotherm was Schneider, and now as Eurotherm is Watlow. Comparatively speaking, are you happy with the resources that Watlow has dedicated to the brand?
Peter Sherwin: Originally, Eurotherm was part of the Invensys setup.
Doug Glenn: Correct; I forgot that.
Peter Sherwin: But it was pretty much a standalone company. We used some of the Invensys products, like Wonderware, etc. As time went on, we were acquired by Schneider around 2014. We were folded into the energy controls business as a whole but still remained intact with Eurotherm. We had our own offices and sales team spread globally around the world. But you could tell toward the end, when Eurotherm put us up for sale and Watlow acquired us, there was a little bit of restriction in spending at that time, which probably slowed down some of the velocity of that program. Thankfully, since Watlow picked us up, we’ve gone full steam ahead. A lot of investment — I’m surprised by the amount of investment.
I’ve been in the industry for 30+ years, and it’s given me the opportunity to work across the world. I started in Europe running commercial heat treatment plants, as well as captive. Then I moved to India working with a suite of commercial shops. Then, about 18 years ago, I moved to the U.S., and it’s all on the back of heat treatment. It’s amazing to look back on. It’s a fascinating industry, and I feel as though I still owe something to the industry. This next generation of platform, for me, is something I’ve worked on for 10 years.
The Edge Process Management (EPM) Platform (6:38)
Doug Glenn: Let’s talk about that. We both attended an industry event where they said the next 4 years will determine the next 20. Essentially, this next period of time is going to be pretty critical. This product you mentioned, EPM, which stands for Edge Process Management, correct?
Peter Sherwin: Correct.
Doug Glenn: Can you describe what it is?
Peter Sherwin: EPM is a platform product. Eurotherm was started in 1965 and produced a lot of instrumentation for the heat treat industry. Everything was kind of separated. We had the best in temperature control, the best in chart recorders, originally from a brand called Chessell, and also the best in SCR power controllers. That was perfectly good for the time those came out.
But as we’re moving into this newer arena, there needs to be far more connection between those devices to address some of the challenges the heat treat industry has to wrap its arms around. For example, how do we improve compliance and make it far easier? What about energy efficiency? That 4 years will dictate the next 20 years to 2050. Think about how the planet will be at that point in time. We will need to run our furnaces far more efficiently.
Doug Glenn: I think more than any other time in history, nobody knows what the industry is going to look like 20 to 30 years down the road. It used to be that you could make a decent prediction. I’m not sure we can do that anymore.
EPM is a platform, not a discrete instrument or product, correct?
Peter Sherwin: The four years are key because of how we are rolling it out. We are releasing one part after another, so it will take a bit of time for the full platform to emerge. We start the release in July with data management, which really replaces our historical chart recorder but goes much further than that. And then, as that evolves, we will bring out control, data, and full automation over the next few years. That’s why it’s a platform. It all shares the same IO base and has the same programming software. There’s commonality across everything, but effectively you can put any of these modules anywhere that makes sense.
Doug Glenn: What is the motivating force behind the development of the platform? I’m assuming that your company, and perhaps companies even before that, had concepts of changes that would occur over the next so many years. Were some of those developments the impetus behind creating this platform? For example, workforce changes — did they have an impact on the motivation to get this platform up and running?
Peter Sherwin: I think we benefited from some of the slowing-down process of Schneider because we were able to make some architectural choices that fit with this kind of new world of AI. A lot of the web technology we’re using fits very well with AI, so we are fortunate that we can leverage some of the current technologies. As we sit here today, AI is in the news all the time. Not everyone is completely sure how all of that is going to unfold but producing a platform today and not being able to utilize AI would be a big miss. So, we integrated those features within the platform, which helps us address many of these workforce challenges.
Considerations When Building This Platform (11:35)
Doug Glenn: Those were actually two of the areas I wanted to ask you about: the workforce and AI. Some other areas I was thinking about were electrification, energy, compliance, and cybersecurity. I assume these were all considerations when you were putting together this platform.
Peter Sherwin: These areas of concern have just accelerated the need for deeply embedding all those technology features. I’m not sure we fully understand how the workforce changes are going to affect the industry and specifically in a furnace operation, but there’s definitely going to be a component of AI that is needed. As we have so many people retiring, there are people coming into the industry who have no background in processes like signing off on records and certain procedures. We have to get new hires up to speed very quickly.
This is the type of technology we’re putting into the EPM platform, to enable people to have a very short learning curve, be useful very quickly, and arm them with the likes of AI so they can enhance their capabilities.
Three forces reshaping heat treatment: an aging workforce, rising energy demands, and the rapid arrival of AI.
Energy is another one of these points. If you have separate components, it takes quite a lot to then integrate those components together to make them more useful, like a temperature controller and a power controller. You can do it, but it doesn’t have all the abilities of one device. You’re only passing certain information between the two. But if you can make that power controller have all the capabilities of control and data, you can more easily manage energy efficiency going forward. This energy piece is another trend this new platform will address.
Doug Glenn: How about the cybersecurity and compliance issues you were talking about?
Related Reading: CMMC Phase II just hit pause. As Sherwin notes in this interview, cybersecurity requirements are only rising for heat treaters — this piece breaks down what the pause does (and doesn’t) mean for defense-supply-chain compliance.
Peter Sherwin: I think that’s one major benefit of a new platform. Cybersecurity requirements have been around for a while. A few years ago, we had SB-327, a California law that came out because of hacking concerns with baby monitors. This law affected everything, though. If an industrial supplier had an internet-connected device, they had to ensure more protection on those devices. You wouldn’t believe the hurdles to then re-engineer cybersecurity into older products — not easy and kind of clunky. Our clients probably still want to slap our wrists on that, because it’s not easy for them. We had to do it because it was a law in California and CMMC. There are laws just coming into effect toward the end of next year in Europe. All of these are going to push higher and higher requirements for cybersecurity. The benefit of a new platform is that you can design these cybersecurity requirements from the start. We’re fortunate there, but it’s a big deal.
Impact of Aging Infrastructure (15:26)
Doug Glenn: How about the fact that the infrastructure most of the systems are built on now is aging out. You’re fortunate to be able to almost start from scratch. Can you comment on the aging infrastructure?
Peter Sherwin: I was having a discussion with a large global heat treater a couple of weeks ago, and they were talking about how one of their issues is that every furnace is different. With this new platform, we are looking to solve those challenges and requirements for clients. We also realize some clients want something they can just take out and put in place, a discrete instrument. So, along with EPM, we’re building what we call our level three controller. That’s a project in flight at the moment. We’ll preview it at Furnaces North America, and it’ll be released sometime in 2027.
The NanoDac recorder | Image Credit: Watlow
If you look at all the different products now across Watlow and Eurotherm, discrete products like the F40, the NanoDac, 2704, 2604, 3504 — these devices are very different, and it’s a learning curve for someone to learn and program them. We are taking the best of these devices and putting them into a new device. Even if you just want to replace a single device, we’ll have that next year.
We are trying to account for the fact that if you’ve got an operator who’s been running a particular controller for several years, how can we make that display appear the same to them? I’m hoping we’ll have some prototypes at the show so we can demonstrate. It’s kind of exciting as we come to the end of a whole suite of products from the Eurotherm and Watlow portfolio and what we’re moving into next. Not just the platform but also being able to keep some of those instruments going with a slightly new disguise.
Doug Glenn: Right, that fits into this platform. Sounds very interesting.
EPM Platform Benefits for Users (18:00)
Doug Glenn: You have talked about the clients and the users of these devices and technologies. If you can summarize briefly, why do they need this platform?
Peter Sherwin: It’s about what you were saying earlier — the next 4 years dictate the next 20. There are so many challenges for heat treaters: dealing with a lack of personnel, new hires not having the skill base, and trying to train faster. These issues are going to hit everyone.
We are not going to see a slowdown in energy initiatives, though possibly a pause in some places in the world at the moment. Why wouldn’t you want to be more energy efficient? We’ve been developing some algorithms for this new platform to enable energy efficiency. It’s not just about climate — it’s about running your operations more efficiently. The two concepts we mentioned earlier: cybersecurity, which is going to rise, and AI. How are our clients or prospective clients going to be able to leverage AI for their operations?
I don’t think that exists in today’s technology, but we’re building it. I’ve seen some of these prototypes where, instead of having to drag function blocks onto an engineering diagram and manually software-wire, it’s just a prompt. Say what you want, and it will create that architecture. It blows my mind when you see it. It’s not a big leap from what’s available to us at our fingertips today, if you use ChatGPT, Claude, etc. But it’s bringing that technology into our industry.
Building the EPM Platform (21:24)
Doug Glenn: We’ve talked a bit about what EPM is. I’m curious about what it took for Watlow to build it. It seems daunting. Can you discuss that process in terms of scalability, data integrity, etc.
Peter Sherwin: Like anything, it takes a village — a global village. We started in 2016. We had a team in India that put together a questionnaire and went around the world to key clients asking in-depth questions.
Doug Glenn: Getting thevoice of the client.
Peter Shirwin: We wanted to learn what was needed for a next-generation product. Now, as we’ve mentioned, things slowed down a bit with the handover at the end of Schneider and into Watlow. Since then, it’s been full speed ahead with Watlow.
Watlow also bought control capabilities; they had their own control line. They manufacture their products in Winona. So now we’ve introduced a Winona team into this village. The original creators were based in Worthing. These were the designers and were responsible for product/project management. We have initial manufacturing where all the Eurotherm instruments are manufactured in Poland, and we also have engineering resources in India. You can imagine all of these groups collaborating. It’s a 24-hour cycle just to build this platform. For anyone looking to build something similar, do not underestimate the amount of effort and money it takes to create something like this. It’s a commitment.
You asked about our commitment to heat treatment. This platform alone is a big commitment, because of the increased ability to do TUSs, SATs, along with process control and process recorders.
Doug Glenn: I did want to ask you about that.
Peter Sherwin: All of that has been considered in this platform. It doesn’t just apply to heat treatment, because from the Eurotherm side, historically, there were two main industries we focused on: heat treatment and life sciences. Life science is all the requirements for auditing. If you make one single change on a device, you have to make sure it was the right person that made that change and have full records. So we really just expanded on all of that intelligence that we already had to a point in some of our data management products. Where heat treatment is kind of moving, life science has already been somewhat out in front. All of that functionality supports the direction we feel heat treatment will potentially go.
Doug Glenn: It seems like a very daunting task to put something like this together.
Peter Sherwin: Daunting but exciting, and you always want to release things as soon as possible. There’s a lot of work in testing the platform. We have a brand to uphold, and we need to make sure we get something out there that works consistently. You know what the heat treatment game is like, Nadcap requirements, AMS2750 — it has to be right.
Compliance for Both Captive and Commercial Heat Treaters (25:46)
Doug Glenn: You mentioned compliance, AMS2750, and Nadcap. How will this EPM platform help a captive heat treater, as well as a commercial one?
Peter Sherwin: For us, it doesn’t really matter. It’s a furnace, and it’s about how best to control that furnace. It starts with the analog input card. You have to get that right for everything else to follow. Much of the development was around how we make a card that meets the requirements of not just a process controller, but also a field test instrument. How can we get that accuracy level in that card so it can be used across anything and isn’t restricted? And, obviously, we need to do it in a cost-effective manner so a commercial or captive heat treater can actually afford it.
We had an R&D project that looked at various ways of doing this. The analysis concluded that errors with the cold junction compensation (CJC) had the biggest impact on accuracy, so we developed a method of CJC. Essentially, it’s like having a very accurate sensor at every junction. That’s what we have on our IO cards. It’s patented, so anyone can go out and look at the details of the patent. Ultimately, it meets process control requirements and field test requirements, which then means you have the possibility, per furnace, to do process control, SAT, and TUS on the same platform. There are some restrictions on Nadcap. You have to mark that this module’s doing an SAT, these modules are doing a TUS, but it’s all common, so you can then share that information, which improves compliance.
AI and Running Processes (28:25)
Doug Glenn: I want to come back to AI. How is this platform going to help end users in running processes? Is there anything in the system, as far as AI goes, that would help?
Peter Sherwin: The ability to pull data and control together means we can start to look at the set-point program as the cycle is running. One of the new functions we’re creating is something called Batch Validator. Think from an operator’s point of view about what they need to do as the process is running, when it finishes, and to sign off and make sure it meets all its requirements. Typically, today, they may need to refer to some other guidance that specifies that this run must be within this tolerance at this level or that it’s had a guaranteed soak between these soak time points. It’s not easy for an operator to see whether the requirements has been met or not, because it’s just a line. That’s the one thing AI and Batch Validator will do — overlay the specifications for that run of the process and show very clearly if it’s deviated or not. It takes that kind of human error and guesswork away from an operator and gives them more information.
Doug Glenn: Do the current standards allow for an automated check on the validation of the load, or will that have to change?
Peter Sherwin: I fully believe in “human in the loop.” Obviously, I’m human. So, there will always be a check. But this really helps as an operator aid, because it’s just checking the screen. Ultimately, the person signing off has to be a real person.
The Future of Heat Treating (31:11)
Doug Glenn: To wrap up, tell us what you think the future of heat treating will look like.
Peter Sherwin: That’s a really good question. It depends on the time scale. I think we’re kind of clear about efficient and reliable running for the next few years. We know we can help with that, but how is it going to structurally change?
I think the operator is going to have a much, much bigger role. There have been less operators in plants. But think about maintenance skills, quality experience, and personnel retiring from the industry. We will have a real problem unless we arm the operator with the ability to do quality, a level of maintenance, and even purchasing.
I think the operator of the future is going to be very multi-skilled but also assisted by AI. We’re seeing it in different areas of our business, how people are moving and taking on more responsibility because of AI. Correlate that with a future where the operator’s going to be that key person and may not require anyone else around to fully run that furnace and run it profitably, with a minimum amount of energy, and making sure it has all the right consumables to keep on running. They’ll manage that whole operation. I think that will be a trend into the future.
Doug Glenn: That is actually a very interesting trend, because with the use of AI, I would expect to see less operators, but I like your perspective. We’ll have to come back in ten years and see how well it panned out.
About the Guest
Peter Sherwin Director of Strategic Marketing Watlow
Peter Sherwin leads strategic marketing at Watlow and brings more than 30 years of experience across heat treatment, industrial technology, business development, and product marketing. His career has included leadership roles with Watlow, Schneider Electric, DOWA HighTemp Furnaces, and Aalberts surface technologies, with experience spanning the United States, United Kingdom, and India. He also holds an MBA from Henley Business School.
In this episode of Heat TreatRadio, host Heather Falcone sits down with Casey O’Neill, vice president of Sales and Marketing at RoMan Manufacturing, to discuss a holistic approach to energy efficiency in thermal processing. Casey explains how furnace power systems, transformer placement, electrification strategies, and data-driven monitoring can significantly impact operating costs, maintenance requirements, and overall performance.The conversation also explores how emerging technologies such as digital twins and AI-driven analytics may help heat treaters address workforce challenges while improving energy management and productivity.
Below, you can watch the video, listen to the podcast by clicking on the audio play button, or read an edited transcript.
The following transcript has been edited for your reading enjoyment.
Introduction (00:04)
Heather Falcone: Hi, I’m Heather Falcone. Welcome to Heat TreatRadio.
Today we’re talking about holistic solutions to thermal processing system energy efficiency. We’re on-site with the sponsor of today’s episode, RoMan Manufacturing, in Wyoming, Michigan. We had the opportunity to take a tour before recording, so thank you to RoMan for hosting us. Joining me today is Casey O’Neill, vice president of Sales and Marketing. Thanks for joining me today, Casey.
Heat Treat Radio host Heather Falcone (left) sits with the vice president of Sales and Marketing at RoMan Manufacturing, Casey O’Neill (right), on-site at RoMan Manufacturing in Michigan
Casey O’Neill: Thank you for being here. We’re excited to host you and record this on the RoMan campus.
Heather Falcone: You get the whole episode to talk about energy efficiency, but I’d like to start by having you tell us a little about yourself and your background. Since we’re at your facility, you get to do your own introduction and tell us a little about RoMan.
Casey O’Neill: I’m Casey O’Neill, vice president of Sales and Marketing at RoMan Manufacturing. I’ve been with RoMan for a number of years, and where I actually started working at RoMan was selling our products into the furnace market.
This segment of industry is really near and dear to my heart. It’s where I got my start in the business. I’m really excited to be able to discuss all the ways that RoMan Manufacturing specifically works to improve the efficiency of electrically heated furnaces within the heat treating industry and how we can help heat treaters money, while improving operational efficiency.
Heather Falcone: I think there are a lot of misconceptions about what can actually be done with energy efficiency. I’d love to dispel some of those myths and help educate people so that making changes to an energy system does not seem so intimidating.
Casey O’Neill: Absolutely. RoMan has a strong history in a variety of different industries, which allows us to take lessons that we’ve learned from one industry and apply them to other industries where that knowledge may not be understood yet.
That’s what we’ve been working to do within the furnace segment is apply our understanding of power and efficiency and how they relate to each other on different pieces of equipment and introduce those to improve the operations of heat treating companies.
Company History (2:53)
Heather Falcone: That’s really how the company got started, right?
Image Credit: RoMan Manufacturing
Casey O’Neill: Correct. RoMan was founded in 1980 by Dietrich Roth and Robert Hoffman. Dietrich was a brilliant engineer in the power transformation space. His focus was on optimizing power density in a resistance welding transformer.
He developed ways to create the power that was needed in a more compact box. As RoMan has evolved over the last 46 years, we’ve taken that concept and technology and expanded it across the products that we offer different industries.
RoMan supplies products for resistance welding, which is where the company started. We also provide power supplies for glass manufacturing, heat treating furnaces, sintering furnaces, and crystal growing furnaces. Outside of thermal processing, we develop power supplies for specialized projects that require high current. One of RoMan’s strengths is rectifying high-current AC power into high-current DC power.
The expertise needed to do that is fairly limited globally. So, we take on very specialized projects for the government and large primes to put that technology into practice for their applications.
Electrification Trends (4:58)
Casey O’Neill: As it relates to heat treating furnaces, over the last five years, there’s been a significant shift toward electrically heated furnaces. In some cases, companies are looking at converting gas-fired furnaces to electrically heated furnaces. A large part of this trend has to do with broader global electrification initiatives and decarbonization goals. As more and more furnaces are electrified and as more people are looking to buy electrically heated furnaces, our goal is to ensure this industry understands how to optimize that electric power so that the energy is being used by the furnace and not just burning into the air.
Water-Cooled Transformers (6:05)
Heather Falcone: When we talk about energy efficiency and thermal processing, beyond heat loss and leakage, what are the biggest inefficiencies that you’re seeing in heat treat operations today?
Casey O’Neill: From RoMan’s perspective, our core product is high-current, low-voltage, water-cooled transformers. We also integrate those power devices into power systems, like controls, breakers, PLCs, and communication with the main control system.
Single phase water-cooled transformer | Image Credit: RoMan Manufacturing
One of the key features is water cooling. In a lot of industries, there are applications where having a water-cooled transformer device is going to make the system more optimized than an air-cooled transformer would. While going to an air-cooled transformer may have a lower upfront cost, if you look at the total cost of ownership versus a power supply that integrates RoMan water-cooled device, because of the equipment optimizations, that will save money on energy and maintenance in the long run. RoMan builds our transformers in a way that integrates the water cooling right into the circuit of the transformer, creating a low-profile, high-power density product. The transformers are fully potted and completely sealed off from the elements. Like I said, we used to test our transformers by submerging them in a fish tank and powering them up to show that water could not get into the transformer and short it out. This allows the transformer to not have to be in a box, so it can be mounted on any equipment, because as long as you’re running the cooling water through the transformer, it’s not going to overheat. It’s impervious to any of the outside elements that you would normally put a different type of transformer in a box for. This allows the transformer to be mounted very close to a power feed-through of a furnace.
Related Reading: New to furnace power systems? Click on the image above to learn about how electricity travels through a vacuum furnace and why transformer placement matters.
Mounting the transformer in this way has many advantages. The secondary side of a transformer, the output side, is the high-current side of the furnace, and the high-current side of an electric circuit is the drastically more inefficient side of the electric circuit because current is heat.
On the primary side, it’s going to be higher voltage and lower current, but when you’re stepping down the voltage and stepping up the current, which is what you do in a transformer on a resistance-heated furnace, you’re creating a high-current, low-voltage secondary.
Well, high current is high heat. If you’re running high-current through big secondary cables from the transformer to the furnace, every foot of that cable is generating heat and wasting energy. So, you’re paying for energy that’s just going into the air.
Heather Falcone: And you’re heating the whole plant at the same time for nothing.
Casey O’Neill: Exactly. You then either have uncomfortable employees, or you need to get a bigger air conditioner.
What RoMan is working to integrate into this industry is remote mounting of transformers directly next to or close-coupled to the power feed-through of the furnace. By doing that, you’re eliminating the secondary cables altogether.
Not only does that save you energy losses in those cables, but those cables are also water-cooled, so you eliminate having to water-cool the cables. If they are air-cooled, and then they’re kicking heat off into the building. They’re also a large maintenance item and can be difficult to handle.
I’ve been to many different heat treating facilities, and I’ve talked with operators about integrating RoMan transformers where you’re closely coupling them to the power feeders and eliminating the cables. One of the operators joked with me and said he’d be my best friend forever if we could eliminate all those cables because of how difficult they are to do maintenance on. When you have a cable fail, it must be removed and replaced. RoMan is trying to eliminate those cables and mount this transformer right next to the power feed-through, making it more efficient and easier to maintain. We can do this because we’ve created a high-power, low-profile density transformer, that is impervious to elements; it can be outside of a box and mounted right next to the furnace.
Heather Falcone: That makes sense. When you’re looking for a system, you’re looking for something that decreases that, the past of least resistance, right? Faster, quicker, and easier.
Workforce Challenges (12:14)
Heather Falcone: The population of working people is shrinking, and in the next 10 years, it’s going to be the lowest it’s ever been. As such, it’s going to get harder to find and hire maintenance personnel, furnace operators, and electrical technicians. If companies eliminate that problem entirely, it’s a win-win.
Casey O’Neill: Exactly. One of the biggest challenges I see in the near-term future is a massive loss of knowledge base.
Across the heat treating industry, there are incredibly knowledgeable and experienced people that have kept heat treating companies running for the last 50 years that are approaching retirement.
As they leave the workforce, that knowledge base is not easily replaced. It’s quickly becoming one of the major challenges of not just heat treating but every industry.
Adoption of New Furnace Technology (13:30)
Casey O’Neill: At RoMan, our core product is high-current, low-voltage, water-cooled transformers, but we are also integrating some of this core product into broader power systems. As we start to integrate, we are also trying to create enough data feedback. We can monitor current, voltage, resistance values, and other parameters. With digital controls, that data can be extrapolated and fed back into the furnace system.
This is where we get into some fun technology. There are companies that can create a digital twin of a piece of equipment by engaging with the equipment and component manufacturers and the OEMs to understand how all of it behaves together.
If we see the transformer temperature internally rising, what does that mean or what could be causing that? If we see a certain part of the furnace inside getting a lot hotter, there are thermocouples inside reading the temperature.
They create a digital twin, and by integrating artificial intelligence, it can start to run simulations in real time, which allows them to communicate next steps with the operator based on what they are seeing with their simulations. So, as experienced personnel enter retirement, we’re going to need to start integrating more of this type of technology into systems or these systems will not operate as well as they should, which means productivity will decrease significantly.
One of the things that RoMan is actively pursuing is determining what feedback our power system can provide to the overall furnace system that’s going to feed into this digital twin and AI simulation to ensure that the furnace can continue to operate in a high productivity fashion, while simultaneously eliminating the challenge of our aging knowledge base.
Related Reading: As furnace systems become more connected, digital controls play an increasingly important role in energy efficiency. Click on the image above to learn about how communication between power systems and furnace controls can reduce energy consumption and support sustainability goals.
Heather Falcone: Right. When you’re undertaking a project like energy efficiency, our tendency might be to focus just on that piece of equipment, and we may miss the opportunity to look at the larger picture and incorporate every piece of equipment.
Casey O’Neill: Exactly. As electrification of heat treating furnaces continues, the operational expense of electricity becomes one of the biggest expenses for heat treat operations. So, managing electric consumption has to become one of the most critical issues to address in order to be profitable.
At RoMan, we don’t just try to sell our products, we try to really engage with the users of our products in every industry that we work in so that they understand from our side how to most efficiently use our products in their system as a way to help the overall OPEX of electricity in a heat treating company, keeping it as minimal as possible or at least as efficient as possible. That way, they are only paying for electricity that is actually used to heat treat products and not paying to kick off energy into the air.
Heather Falcone: Let’s talk a little bit about that whole process that you’ve come up with, because many heat treaters are working with legacy systems. It can seem daunting to go from a VRT to this solution. What do you recommend for how they can attack this issue without being intimidated and rejecting the project entirely?
Casey O’Neill: In a number of industries that we work with, this fear is common. Nobody wants to be first. Everybody wants to be second. If we have new technology, they don’t necessarily want to be the first one to be innovative and integrate it into their system because they also don’t want to be the reason that production goes down if it fails.
So, you have systems with legacy products that have been working for decades and that makes people very comfortable with that system. Even if we can make drastic improvements on the overall operational expense, they are motivated by job security, so they want to work with products and technology they feel comfortable with.
We engage with both small and large captive and commercial heat treaters, and what I found is when you engage with a company that has a group that’s really focused on energy efficiency, especially as electrification grows, those people end up having a lot more say in what technology ends up being integrated into a system.
RoMan has a number of systems integrated into heat treating furnaces, and within our product offerings we have different types of power systems used in various applications. We have air-cooled transformers manufactured at our facility in Grand Haven, Michigan, and we have our high-current, low-voltage water-cooled transformers that are 60 hertz, 480-volt input, very basic, that are integrated into different furnace systems.
Our legacy began in resistance welding in the automotive world. If you’ve ever seen the commercials for vehicle manufacturers where you have the metal frame of a car going down an assembly line with robotic arms moving around, sparks flying — that’s resistance welding. It’s a bad example because when you’re doing the welds, you don’t want sparks, but sparks are good for TV.
That’s a resistance weld. On that robot is a weld gun, and in that weld gun is likely (if it’s in North America) a very small RoMan transformer. The input into that transformer is 1,000 hertz, 620 volts, and it’s coming from an IGBT control. In that industry, in the late ’80s/early ’90s, they started to go from larger stationary spot-welding stations to this robotic welding. They’re putting thousands of amps through that little weld gun, and if they were to use an AC transformer, it would be large, and then they would be running those big heavy cables to the weld gun. Also, robots move around so much, so you end up having a lot of maintenance issues and inefficiencies.
By integrating this IGBT control that can put out 1,000 hertz, you can exponentially shrink the core of the transformer, which makes the transformer size shrink significantly. Now since the ’90s, almost every automotive line is using an IGBT control and a really small, high-current, low-voltage, water-cooled transformer that’s in the weld gun connected to where the weld tips are.
RoMan takes something that’s been used for 40 years in one industry and realized there were many applications that would benefit from integrating this type of technology onto a furnace. When you’re running continuous heat for a long time, you end up creating a lot of inefficiencies, which can get really technical.
This system overcomes many inefficiencies using these different types of technology that all look different from what people are used to seeing. What we are trying to do is educate people on the uses of these new technologies and the benefits of their integration on furnaces, particularly the improvement of operational expenses, electrical efficiencies, and also maintenance.
Furnace Integration — Retrofits (24:24)
Heather Falcone: We have discussed RoMan and the systems. Tell me about integration at the furnace level.
Casey O’Neill: As I have mentioned, we have different furnace applications that use different versions of our technology. For commercial heat treaters, heat treating is how they make money, managing operational expenses of the equipment is absolutely critical to profitability.
We have conducted several trials and product integrations where we’ve worked directly with heat treaters to retrofit equipment so they can start to understand and see the operational gains from using our products.
Related Reading: Casey discusses how power controls affect furnace efficiency and performance. Click on the image above for a deeper look at IGBTs and matching controls to heating loads.
In one case, we had a commercial heat treater that had a vacuum furnace with a large legacy power supply. We have some different data loggers that measure and log power data. We hooked them up to a power logger, and had them operate normally, run different parts that they normally run through that furnace, and we just logged the power data, the kVA, kW, the reactive power, and the power factor — parameters that matter when it comes to power and what the utility companies bill people for. Then we examined that data, and we saw that the power supply that they had for the work that they were doing was oversized.
One of the changes that we could make to optimize the operation of the power supply was to size it more according to their needs. In this case, we retrofitted that furnace with an IGBT control and our small MFDC transformer. We sized the kVA down more to their needs to add some efficiency to the system.
We had them turn it back on and operate normally, using the same parts. We logged the power again and we were able to compare the different runs. We also had them run a burnout run so we could compare the burnouts. As we were doing that, we noticed one run that they were doing after we retrofitted the furnace that wasn’t in any of the logs from before we retrofitted it. We did not know where it came from. The company explained that there is a certain part that they heat treat for a customer and that particular process has a very, very fast ramp rate to get to a very high temperature. In their facility, they only have one furnace that they can do that particular cycle on. After we retrofitted this furnace, they decided to try this particular process, and they were able to do it.
Heather Falcone: That’s awesome.
Casey O’Neill: Keep in mind; this was actually a smaller kVA transformer than what was on that furnace before. The output of this transformer is DC, not AC, and AC has a sine wave, which kind of goes like that, and there’s a zero point where it’s always crossing zero. Every time it crosses zero, it’s off for just a little bit. We’re talking milliseconds. But then it has to reheat a little bit. It continues heating, and then it’s off as it crosses zero again.
The MFDC, however, is DC power. DC power is just on, so you’re just managing how much it’s on. As a result, you never have to do that little reheat before you continue heating like you do with AC power.
The best explanation, and this is anecdotal, not data-driven, is when they retrofitted that furnace with DC power, because that DC can just stay on, they were able to ramp much faster and hotter. Even though it was a lower kVA transformer, it still had enough power to get up to temperature. Also, because of the DC power, it was a much better heat, so it could get up to temperature much faster and allow them to do that cycle.
So operationally, they’re now able to run that particular process in two furnaces instead of one. Double the capacity, which is now optimized for a commercial heat treater to be able to shift things around more and have some options when they’re in production.
A burnout run is obviously a lot more power than a different run that’s lower temperature or a shorter period of time. On some of those lower temperature runs, there was about a 4% kilowatt hour difference in consumption after we retrofitted it. For the burnout run, the kilowatt hour difference was actually 18%. By retrofitting, it reduced the real power consumption by 18% on every burnout.
The ability to use DC in a resistance application is always going to make it more efficient. But then also by shortening the secondary, you’re eliminating energy waste, optimizing efficiency, and enhancing maintenance.
In that facility, about half of the furnaces now have that IGBT system and these small MFDC transformers. This is a more expensive system. When you look at the total cost of ownership, you have to weigh the costs, is the value there or not? There are certain cases where it is, and there are certain cases where it is not. As I mentioned, half of their furnaces have this system, and the other half do not. That’s because for half of them, there isn’t enough added value to make this change. But for other furnaces at other heat treating companies, we have AC high-current, low-voltage water-cooled transformers like this where those are integrated. We’ve closely mounted them to the power feed-through, and just by getting rid of those secondary cables, we’ve helped improve the efficiency of the furnace. That one is actually really easy to do the math on savings because different size cables have ratings of heat loss per foot. You can easily do the math. On every foot of cable we eliminate; you’re going to save this much in energy consumption.
In some other cases, the transformer, while important, actually becomes less important than the power control. In some commercial heat treaters and in many captive heat treaters, they may run one part over and over and over again. But they have to make sure that every single time it is done exactly the same. We integrate our transformers into a power system where we’re getting controls and other feedback devices that we integrate into the whole system so you can start to monitor and digitally send all that information into a main equipment PLC, which means they can make a lot more informed decisions on how to manage the equipment to ensure that the output is consistent time and time again.
Heather Falcone: Absolutely. I think that’s important to insert in part of the process. As somebody who’s looking to evaluate energy efficiency, you are looking at retrofit, so you need to get the right partner that can give you the education and the foundation to dispel any worries that you might have right up front.
Furnace Integration — New Equipment & OEMs (34:10)
Heather Falcone: But it’s not just retrofits; you can integrate with brand new equipment. Can you talk a little bit about this and working with OEMs.
Casey O’Neill: In general, RoMan has a history of trying to work with everybody in the supply chain, because I think that we offer different forms of value to different companies within that supply chain. The heat treating industry is no different. Furnace users and heat treaters are the ones that are paying their electric bill. They’re the ones that are paying the furnace operators, the maintenance personnel. They’re buying maintenance parts. For them, the value of the RoMan products is going to all be things that impact their P&L.
But we also work with a number of different furnace OEMs where the value is more on them being able to sell a more optimized piece of equipment to an end user. So, in general, most of the furnace OEMs, especially here in North America, are very familiar with RoMan products. They’re going to understandably be very sensitive to their customer needs and the demand. If I have the best product in the entire world, but my customers don’t want it, I am not going to go out of business holding the best product in the world.
As we work with OEMs, they understand how to integrate the RoMan products in various ways to optimize the equipment. At the end of the day though, it’s based on the demand from a heat treating company that will be buying that equipment. Whether they are going to integrate RoMan, add value, be more technologically advanced, or stick with the legacy equipment because that’s what they know.
Again, the education still needs to go back to the heat treating industry, commercial heat treaters and captive heat treaters, to understand the importance of having a highly optimized piece of equipment in their overall system and what it can do for their bottom line.
That’s really how the demand is going to shift. I’m part of RoMan, and so I will always say how they’re going to demand shift to RoMan. But in general, the demand is going to shift from how companies have done it for 50 years and are comfortable with seeing real value in this new technology because it’s going to optimize their system. It’s going to help them manage the system because they are losing a knowledge base. It’s going to give a better output. It’s going to lower their OPEX. As companies become more and more focused on optimization and value, I think you’re going to see a shift to more advanced technology.
However, what I tell everybody is that what RoMan sells is not new technology; it’s 40 years old. It’s just new to this industry.
Biggest Takeaways (37:48)
Heather Falcone: On that subject, what is the big takeaway that you want our readers and listeners to know from everything that we’ve talked about today?
Casey O’Neill: At Roman Manufacturing, our vision is to be the global brand of choice for industrial power conversion solutions, and the heat treating world is a part of that. We never try to just sell a product. Our goal is to work with our customers and the companies that are using our equipment, even if they’re not directly our customer, to make sure that they not only understand the added value, but also can capture the added value, because it’s one thing to know that a product can add value, and it’s another thing to make sure you’re actually capturing it.
At the end of the day, RoMan is focused on our customer and our user base. We will always support our products to make sure that people are squeezing every drop of value out of it that they can. I think that’s how you create really good partnerships when you’re working together to optimize a furnace system, for example. You start to learn from each other to be able to even optimize the equipment that they’re buying in a bigger way.
Heather Falcone: You can learn from each other, continually improve. We can’t do it without working together.
Casey O’Neill: Exactly.
Heather Falcone: Thank you so much, Casey. I really appreciate you spending time with me today.
About the Guest
Casey O’Neill Vice President of Sales and Marketing RoMan Manufacturing
Casey O’Neill is vice president of Sales and Marketing at RoMan Manufacturing, a Grand Rapids, Michigan-based manufacturer of high-current, low-voltage power conversion equipment serving industries including resistance welding, glass, furnace, and other industrial applications.
Casey leads RoMan’s sales, marketing, and market-development efforts with a focus on strategic growth, client partnerships, and expanding RoMan’s presence in global industrial markets. His background includes leadership roles in sales strategy, business development, operations, and manufacturing, giving him a practical understanding of how technical solutions, commercial strategy, and client needs intersect.
For Heat TreatToday, Casey brings a perspective shaped by working closely with industrial clients in demanding thermal-processing, furnace, and power-conversion applications.
Smart controls, connected systems, and hybrid energy strategies are reshaping what American manufacturers expect from their process heat equipment. In this Technical Tuesday installment, Markus Kirk, international business development manager for Digitalization and Process Heat at Phoenix Contact, outlines how U.S. process heat OEMs can move beyond basic temperature control toward fully optimized, data-driven thermal system — covering the role of IIoT connectivity, machine learning, virtualization, and cybersecurity standards in building equipment that is audit-ready, energy-efficient, and built for long-term lifecycle value.
This Sustainability Insights article was first published inHeat Treat Today’sMay 2026 Sustainable Heat Treat Technologiesprint edition.
Across the United States, process heat OEMs are shifting from purely mechanical design to software-driven, performance-centered solutions. American manufacturers in aerospace, automotive, medical, defense, and heavy industry expect systems that adapt quickly, deliver consistent results, and support long-term energy and sustainability goals.
Hybrid heating — combining natural gas or hydrogen with electric boosting — is gaining strong momentum in the U.S. because it improves temperature uniformity, shortens recovery times, and reduces emissions. These advantages align with rising energy costs, state-level decarbonization initiatives, and corporate ESG (environmental, social, and governance) commitments. Smart electrode placement and advanced proportional–integral–derivative (PID) strategies help stabilize throughput during production changes, part transitions, and batch-continuous operations, reducing risk and improving repeatability.
Built for U.S. Compliance: Security, Connectivity, Safety & Virtualization
Real-time monitoring, IIoT connectivity, and machine learning (ML) have become essential in American heat treating environments. High-resolution temperature and energy data help operators detect anomalies early, while ML-driven control loops automatically correct deviations. This supports better part quality, higher overall equipment effectiveness (OEE), and fewer unplanned stoppages.
Security expectations in the U.S. are well-defined. NIST CSF and ISA/IEC 62443 guide cyber security hardening; NFPA 86 and ISO 13577 define burner safety and system architecture requirements. OEMs that build equipment around these standards and provide audit-ready documentation stand out in a market where internal audits, client-specific requirements, and on-site assessments are routine.
Virtualization is another driver in the U.S. market, especially within large installed bases. Virtual PLCs and software-defined architecture allow new functionality like load management or predictive energy control, and updated regulation strategies to be added without hardware lock-in. Code written in IEC 61131-3, C++, Python, or Simulink can execute securely at the edge while feeding cloud dashboards and web-based HMIs. This makes modernization and retrofits faster, cleaner, and easier to deploy across geographically distributed facilities.
Engineering Speed and Lifecycle Value for American OEMs
U.S. OEMs must deliver consistent quality across product lines while reducing lead time. Modular function blocks for signal conditioning, ratio/Lambda control, burner management, autotuning PID, ramping, interlocks, and diagnostics support standardized engineering practices from small batch furnaces to large continuous systems. Adding ML-based anomaly detection helps convert operator experience into data-driven best practices, enhancing uptime and enabling scalable remote-service programs — an increasingly important revenue source in the U.S. market.
Accurate temperature measurement remains the foundation of reliable heat treatment. Certified, cybersecure I/O modules ensure precise signal integrity, support regulatory compliance, and reduce panel complexity. This reinforces both product quality and plant safety — critical in industries governed by AMS, CQI-9, Nadcap, and OEM-specific client standards.
Whether American OEMs manufacture high-volume standard equipment or engineer custom thermal systems, the competitive formula is consistent:
Open ecosystems for rapid integration and IP protection
Security-by-design for audit-ready operation
Hybrid-energy readiness for decarbonization without compromising performance
Virtualization for scalable features
Lifecycle digital services that support recurring value
Open PLC and edge-centric platforms make this evolution practical. They enable U.S. OEMs to reuse proven code modules, expand capabilities quickly, and differentiate in a market driven by uptime, serviceability, and total cost of ownership. As the U.S. heat treat industry continues modernizing, the winners will be the OEMs combining intelligent control, secure connectivity, hybrid energy strategies, and software-defined flexibility — turning process heat equipment into resilient, future-ready performance systems.
About The Author:
Markus Kirk Intl. Business Development Manager, Digitalization & Process Heat Phoenix Contact
Markus Kick brings 25+ years of hands-on industrial expertise across process automation, thermal heat treatment systems, instrumentation, control engineering, and data-driven decision making. He is known for turning industrial digitalization trends into scalable, high-impact solutions that accelerate OEM innovation and deliver measurable value across global manufacturing environments.
When carbon-footprint assessment happens during material selection for CAE simulations and product design, the result is more informed and sustainable decisions.In this Technical Tuesday installment, Mariagrazia Vottari, chief technical officer at Total Materia AG, shows how informed material choices can identify lower-impact alternatives without compromising structural, mechanical, or physical requirements.
This informative piece was first released in Heat Treat Today’sMay 2026 Sustainable Heat Treat Technologies print edition.
Introduction
Governments and industries worldwide are setting increasingly ambitious targets to reduce greenhouse gas (GHG) emissions and strengthen environmental responsibility across supply chains. New sustainability frameworks, mandatory reporting requirements, and carbon-pricing mechanisms are accelerating the shift toward low-carbon production, including stricter expectations for transparent environmental data and lifecycle assessments.
Consequently, global supply chains must adapt quickly, integrating sustainability considerations from the earliest stages of product design through manufacturing, distribution, and end-of-life management. Environmental performance, traceability, and responsible material selection are becoming essential elements of modern engineering and product-development strategies.
Materials themselves represent a major share of global GHG emissions, increasing from 5 to 11 global net anthropogenic GHG emissions (GtCO₂-eq) between 1995 and 2015, and rising from 15% to 23% of global totals. For most products, materials dominate the carbon footprint until manufacturing is complete.
Accurate material selection in early product design and CAE (computer aided engineering) simulations is critical. Beyond traditional factors, such as mechanical performance and cost, engineers must now consider carbon footprint, environmental impact, lightweighting, regulatory compliance, and supply chain optimization to reduce overall emissions.
Therefore, sustainable product design will incorporate Life Cycle Assessment (LCA) of materials using selected indicator(s) providing environmental impact to materials selection. For example, in the automotive industry, ranking (c) is often calculated as c = 0.4 × mass + 0.2 × cost + 0.4 × CF.
Other more complex decision-making models for materials selection have been proposed. This exemplifies the need for reliable and simplified calculation of carbon footprint (CF) value for thousands of diversified structural materials, from carbon and stainless steel to special alloys, nonferrous metals, and polymers, considering their manufacturing routes, processing, finish, and transport. A full LCA study is demanding in terms of both data collection efforts and user expertise requirements, while streamlined LCA often uses generic data related to the materials production, energy used for their processing, and transportation. Typically, streamlined LCA uses only a fraction of the inputs to estimate carbon footprint compared to the full LCA inventory. This article presents recent developments designed to help engineers in the CAE simulation field to cope with these challenges.
Streamlined LCA Methodology
Figure 1. LCIA assessment approach | Image Credit: Total Materia
There are numerous simplification approaches in LCA; the following describes the approach that combines the composition of alloys with carbon footprint values of base metal and alloying elements production. The LCA tool described in the current study (Figure 1) can cover a variety of ferrous and non-ferrous alloys due to the use of:
Chemical compositions from a large database containing structural material properties, which comprises more than 500,000 materials; and
Country, manufacturing route, processing, and transport-specific life cycle inventory (LCI) collected from Ecoinvent v3.10, along with relevant data from scientific articles.
Goal, Scope, Functional Unit and System Boundaries
The aim of this LCA is to quantify the impact of steel and various non-ferrous alloys (Al, Cu, Mg, Ni, and Ti based) according to ISO 14040 standards, analyzing the influence of the composition on the carbon footprint.
The functional unit has been defined as 1 kg of produced material, considering the country of manufacturing and processing as well as transport to the buyer’s gate.
The scope of this study is to estimate the environmental impact of the production and the transport of materials (cradle to gate), accounting for raw materials extraction, manufacturing, and processing.
Inventory Data and Impact Category
Ecoinvent’s Life Cycle Inventory Assessment (LCIA) datasets were used where possible, including:
Base metals
Alloying elements, utilized in the manufacturing calculation through chemical composition weighting
Processing, quantified in kg CO₂-eq per kg of material, per kg of removed material, or per m², varying with the type of processing
The energy mix, allowing country-specific calculation
Transport, covering a wide range of routes
Calculations are based on the cut-off system model, the IPCC 2021 no LT LCIA method, and the climate change Global Warming Potential (GWP100) indicator.
Additional sources were used from scientific literature for data not available in Ecoinvent. The calculation scope expanded with:
Scrap content adjustment manufacturing contributions from various countries/regions
Contributions from different manufacturing routes
Various processes in different countries/regions
For intensive electricity-consuming processes, such as hot rolling, cold rolling, and stamping, electricity consumption data (measured in MJ/kg or kWh/kg) has been collected. This data, combined with the energy mix information from Ecoinvent, contributes to the final calculation.
Figure 2. System boundaries | Image Credit: Total Materia
The final CO₂-eq score is the cumulative sum of contributions from material production (manufacturing), processing, and transport as shown in Figure 2, illustrating the system boundaries considered in the study.
Analysis CF Results
In this work, six different alloys that are commonly used have been selected for the carbon footprint analysis. The chemical composition of alloys is defined by specific standard, while details on studied alloys production are presented in Figure 3.
Figure 3. Result of CF calculation for selected alloys | Image Credit: Total Materia
After specifying details on manufacturing (country, method, and recycled content), processing (country and processing applied), and transport (type and distance), the values of carbon footprint are determined for each alloy (Figure 3), providing the contribution of each stage of analysis.
The lowest environmental impact of all studied alloys was steel 1.4301 with a value of 2.5 kg CO₂-eq/kg. This is because a manufacturing route for the 1.4301 alloy was EAF (electric arc furnace) with 100% recycled content, where electricity is used to melt scrap steel and produce new steel, in contrast to BF-BOF (blast furnace-basic oxygen furnace) where extraction of iron ore is needed and relies heavily on coal or coke as a fuel source for the blast furnace, which emits significant amounts of CO₂ during combustion. Although numerous factors or variables play a role in determining the environmental impacts of metal production, one of the most significant parameters is recycled content.
Titanium alloy has the highest environmental impact of all studied alloys, emitting up to 47.3 kg CO₂-eq/kg of material. Ti-6Al-4V alloy was selected for this study even though it is very expensive and has a high energy consumption of production in the long and demanding Kroll process, because it is one of the most popular joint implant materials due to its biocompatibility, low density, and strength.
Although Al, Cu, and Fe-Ni-based alloys have similar CF values (4.7 to 8 kg CO₂-eq/kg), in the case of aluminum and copper alloys, the most significant contribution comes from the processing of those alloys (52 to 68%), unlike Incoloy in which processing contributes a modest 0.72%. The CF value for Incoloy 800 is three times greater than 1.4301 alloy. The high environmental impact of Incoloy 800 is mainly caused by nickel content (max. 10% in 1.4301 alloy, while max. 35% in Incoloy 800) and very high carbon footprint values for nickel itself. This is proof of why chemical composition cannot be neglected.
The effect of transportation is very small, only contributing up to 3.6% for selected transport parameters. However, it can have much higher relative contribution for low-impact alloys, especially over long distances. In Figure 4, the effect of different transport types shows that the selection of air transport can double the carbon footprint value of the material compared to sea transport (for the same manufacturing and processing parameters).
Figure 4. Effect of different transport types | Image Credit: Total MateriaFigure 5. Detailed contribution analysis for 1.4301 steel | Image Credit: Total Materia
Further contribution analysis can be made for each alloy given the detailed contribution for manufacturing and each processing step, as well as transportation type, as shown in Figure 5 for the 1.4301 steel. Results show that deep drawing increases carbon footprint with a factor of 5 in comparison with hot rolling. This suggests that such processes should be performed on locations having energy supplied from renewable sources.
Material Selection, Looking for a Greener Alternative
Besides identifying more environmentally sustainable manufacturing processes such as alternative production routes, higher scrap content, different locations, processing with lower energy demand, and greener transportation options, another approach to reducing the carbon footprint is to identify alternative materials with different chemical compositions but similar mechanical and physical properties.
Although the selection of alternative materials must consider various factors related to the availability, supply chain, etc., from the environmental point of view, the decision can be facilitated by using a proper cross-reference system that simultaneously suggests alternatives based on various criteria. There are two scenarios for material selection:
In the early design phase when the material is still not selected and when certain mechanical, physical, compliance and sustainability requirements should be fulfilled.
When a certain material already in use should be replaced with a greener alternative but maintain the same characteristics.
In the first case, material-selection tools like the Total Materia Optimizer can be used to support engineers in comparing and ranking materials based on multiple technical and regulatory criteria. This tool can evaluate thousands of potential candidates simultaneously and filter them according to user-defined parameters, such as mechanical performance, chemical composition, cost, regulatory status, or regional availability as shown in Figure 6.
Figure 6. Results of multicriteria search | Image Credit: Total MateriaFigure 7. Alternatives to 1.4301 steel based on cross references | Image Credit: Total Materia
In the second case, when the material is already in use, finding an alternative material with a lower CF value is possible in a material-selection tool’s carbon footprint module through the cross-reference option. The system offers alternatives based on various criteria. As an example for this case, 1.4301 alloy is used with all set-up parameters from Figures 4 and 6 (with CF value of 2.528 kg CO₂-eq/kg). The analysis shown in Figure 7 suggests 921 alternative materials ordered by CF value in ascending order. In this view, a user can add additional columns with mechanical and physical properties to ensure that the material also fulfills the required characteristics. In this example, material NSSC 2120 meets the required mechanical and physical criteria, and the CF value is reduced from 2.5 to 2.2 kg CO₂-eq/kg (which is a reduction of 12%) compared to the initially selected material 1.4301.
Conclusions
This approach for assessing the environmental impact of ferrous and non-ferrous alloys based on material composition and processing routes has been illustrated through a carbon footprint evaluation. It enables engineers to compare materials not only by cost and performance but also by their carbon intensity, supporting more informed and sustainable selection decisions. The method also helps identify greener manufacturing options, such as alternative routes, higher recycled content, lower-energy processing, or reduced-impact transport, early in product design while maintaining quality and performance.
Future improvements include expanding datasets to cover additional processing steps, incorporating more specific manufacturing routes — especially for non-ferrous alloys — and increasing regional coverage to reflect local energy mixes. These enhancements will further refine emission factors and improve the accuracy of carbon-footprint assessments.
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About The Author:
Mariagrazia Vottari Chief Technical Officer Total Materia AG
Mariagrazia Vottari is the chief technical officer at Total Materia AG, leading the Engineering Department and overseeing data content development and material intelligence initiatives. She has a background in mechanical engineering and nearly 20 years of experience in the industry, with a strong focus on materials engineering, data processing, and digital solutions for the manufacturing industry.
Carbon emissions reporting is no longer optional for heat treaters — it’s becoming a competitive and regulatory necessity. In this Sustainability Insights installment, Heat TreatToday examines research from Professor Fu Zhao and PhD candidate Lakshmi Srinivasan of Purdue University’s Heat Treating Consortium, detailing a new python-based carbon calculator built specifically for heat treat operations, how it models Scope 1, 2, and 3 emissions from furnace geometry and process parameters, and how in-house heat treaters can use it to meet growing transparency demands with minimal manual effort.
This informative piece was first released in Heat Treat Today’sFebruary 2026 Annual Air & Atmosphere Heat Treating print edition.
Emissions reporting has become an essential step. Navigating the requirements in an influx political environment only adds to the challenge. How can heat treaters remain in compliance? A tool designed through Purdue University’s Heat Treating Consortium (PHTC) may be the answer.
The consortium has funded research across heat treat projects ranging from the efficacy of novel quenchants to improving materials hardness. Roughly two years ago, the PHTC member companies requested research to develop a tool that would make carbon estimation possible.
Lakshmi Srinivasan, PhD Candidate in the School of Mechanical Engineering at Purdue UniversityProfessor Fu Zhao, Faculty Member at the School of Mechanical Engineering and the School of Sustainability Engineering and Environmental Engineering at Purdue University
Professor Fu Zhao, faculty member at the School of Mechanical Engineering and the School of Sustainability Engineering and Environmental Engineering at Purdue, decided to take on this research request. He brought on PhD candidate Lakshmi Srinivasan, an astute researcher of energy systems modeling and life cycle assessment in the School of Mechanical Engineering, to research and develop the tool. “This project aims to model furnace energy requirements based on furnace geometry and heat treating input parameters,” Srinivasan explained. “From these modeling energy flows and furnace build inputs, we calculate Scope 1, Scope 2 and Scope 3 carbon emission associated with operating the furnace.”
Scope 1: Direct carbon emissions from energy consumption within the plan (e.g. combustion of natural gas or other fuels)
Scope 2: Indirect emissions from purchased electricity, steam, heat, or cooling
Scope 3: All other indirect emissions across the supply chain (e.g., suppliers, transportation, product use)
The tool is a python-based desktop application with scalability in mind. Since development targets the carburizing process for both market and regulatory reasons, it is highly focused on industry needs. Additionally, Zhao and Srinivasan built the tool for users to integrate additional features and data sets to align with new requirements or emerging technologies. They also underscored that the tool’s architecture is designed for growth as a web-based application.
Image of the digital carburization tracking tool | Image Credit: Srinivasan and Zhao
Ease of use is central. Zhao and Srinivasan have refined the tool to limit how much unique user input is required to generate an accurate output. The team explained this as particularly challenging, having examined alternatives to simplify the interface without oversimplify the “underlying physics.” They described how the final form of the tool will work, saying that once key parameters are entered (furnace type, processing temperatures, time, part geometry), the tool will automatically calculate energy usage and emissions with minimal manual intervention.
PHTC members, many of whom represent manufacturers with in-house heat treating, have shown great interest, providing feedback and resources to shape the development of the tool. Additional enthusiasm was found at IHEA’s annual SUMMIT in August 2025, where Srinivasan presented the tool’s development. When asked what inquiries have directed their research, Zhao and Srinivasan shared the following:
Versatility and functionality: How flexible is the tool in accommodating different furnace geometries, part geometries, furnace types, and heat treatment processes?
Part-based allocation: How does the tool allocate emissions accurately to individual parts or batches within a furnace load?
Location-specific emissions: How does it account for location-based variations in scope 2 and scope 3 emissions, such as differences in electricity generation or supply chain impacts?
Another challenge has been ensuring careful tool calibration and verification. To do so, the team has taken accurate, real-world natural gas and electricity consumption from heat treat operations, courtesy of PHTC members, to verify the model’s predicted energy consumption at defined furnace operating temperatures.
Eventually, some form of this tool will be made available to those outside the consortium. Currently, however, PHTC members are at the forefront of development and usage. The researchers underlined this, commenting, “This tool is particularly timely and essential for industry, as companies are increasingly expected to provide transparent and accurate emissions reporting.”
While the world of standards and regulations can feel like a minefield, benchmarked discussions of this tool reveal promising applications for in-house heat treaters in the near future.
El reporte de emisiones de carbono ya no es opcional para los especialistas en tratamiento térmico — se está convirtiendo en una necesidad competitiva y regulatoria. En esta entrega de Perspectivas de Sostenibilidad, Heat TreatToday examina la investigación del Profesor Fu Zhao y la candidata a Doctorado Lakshmi Srinivasan del Heat Treating Consortium de Purdue University, detallando una nueva calculadora de carbono basada en Python, desarrollada específicamente para operaciones de tratamiento térmico, cómo modela las emisiones del Alcance 1, 2 y 3 a partir de la geometría del horno y los parámetros del proceso, y cómo los especialistas en tratamiento térmico con operaciones internas pueden utilizarla para cumplir con las crecientes exigencias de transparencia con un mínimo de intervención manual.
Este artículo informativo se publicó por primera vez enHeat Treat Today’sFebruary 2026 Annual Air & Atmosphere Heat Treating print edition.
Si tiene comentarios o preguntas sobre este artículo, háganoslo saber en: editor@heattreattoday.com.
El reporte de emisiones se ha convertido en un paso esencial. Navegar los requisitos en un entorno político cambiante solo añade complejidad al desafío. ¿Cómo pueden los especialistas en Tratamiento Térmico mantenerse en el cumplimiento normativo? Una herramienta diseñada a través de Purdue University’s Heat Treating Consortium (PHTC, por sus siglas en inglés) podría ser la respuesta.
El consorcio ha financiado investigaciones en proyectos de tratamiento térmico que abarcan desde la eficacia de nuevos medios de temple hasta la mejora de dureza de los materiales. Hace aproximadamente dos años, las empresas miembros del PHTC solicitaron una investigación para el desarrollo de una herramienta que hiciera posible la estimación de carbono.
Lakshmi Srinivasan, Candidata a Doctorado en School of Mechanical Engineering at Purdue UniversityProfessor Fu Zhao, Miembro del Profesorado de School of Mechanical Engineering and the School of Sustainability Engineering and Environmental Engineering at Purdue University
El Profesor Fu Zhao, miembro del profesorado de School of Mechanical Engineering and the School of Sustainability Engineering and Environmental Engineering at Purdue decidió asumir esta solicitud de investigación. Incorporando a la candidata a Doctorado Lakshmi Srinivasan, una destacada investigadora en el modelado de sistemas energéticos y evaluación del ciclo de vida en School of Mechanical Engineering y la School of Sustainability Engineering and Environmental, para la investigación y desarrollo de esta herramienta. “Este proyecto tiene como objetivo modelar los requerimientos energéticos del horno en función de su geometría y los parámetros de entrada de tratamiento térmico”, explicó Srinivasan. “A partir de estos flujos energéticos modelados y de los insumos asociados a la construcción del horno, calculamos las emisiones de carbono del Alcance 1, Alcance 2 y Alcance 3 asociados a la operación del horno”.
Alcance 1: Emisiones directas de carbono derivadas del consumo de energía dentro de la planta (por ejemplo, combustión de gas natural u otros combustibles)
Alcance 2: Emisiones indirectas provenientes de electricidad, vapor, calor o enfriamiento adquiridos
Alcance 3: Todas las demás emisiones indirectas a lo largo de la cadena de suministro (por ejemplo, proveedores, transporte, uso del producto)
La herramienta es una aplicación de escritorio basada en Python, diseñada pensando en la escalabilidad. Dado que el desarrollo está orientado al proceso de carburizado tanto por razones de mercado como regulatorias, se encuentra altamente enfocada en las necesidades de la industria. Adicionalmente, Zhao y Srinivasan diseñaron la herramienta para que los usuarios puedan integrar características adicionales y conjuntos de datos que se alineen con nuevos requerimientos o tecnologías emergentes. También subrayaron que la arquitectura de la herramienta está pensada para su crecimiento como una aplicación basada en la web.
Imagen de la herramienta digital de seguimiento de carburizado | Image Credit: Srinivasan and Zhao
La facilidad de uso es un aspecto esencial. Zhao y Srinivasan han refinado la herramienta para limitar la cantidad de entradas únicas requeridas por el usuario para generar un resultado preciso. El equipo explicó que este aspecto fue particularmente desafiante, ya que se examinaron alternativas para simplificar la interfaz sin simplificar en exceso la “física subyacente”. Describieron como funcionará la versión final de la herramienta, explicando que una vez que se introduzcan los parámetros clave (tipo de horno, temperaturas de proceso, tiempo, pieza) la herramienta automáticamente calculará la energía usada y las emisiones con una intervención manual mínima.
Los miembros del PHTC, de los cuales muchos representan compañías manufactureras que cuentan con tratamiento térmico interno, han mostrado interés, proporcionando retroalimentación y recursos para dar forma al desarrollo de la herramienta. Un entusiasmo adicional se observó durante el IHEA’s annual SUMMIT en agosto de 2025, donde Srinivasan presentó el desarrollo de la herramienta. Cuando se les preguntó qué interrogantes han guiado su investigación, Zhao y Srinivasan compartieron lo siguiente:
Versatilidad y funcionalidad: ¿Qué tan flexible es la herramienta para adaptarse a diferentes geometrías de horno, geometrías de piezas, tipos de hornos y procesos de tratamiento térmico?
Asignación basada en piezas: ¿Cómo asigna la herramienta las emisiones de manera precisa a piezas individuales o lotes de una carga dentro del horno?
Emisiones específicas por ubicación: ¿Cómo considera las variaciones regionales en las emisiones del Alcance 2 y Alcance 3, tales como las diferencias en la generación de electricidad o los impactos de la cadena de suministro?
Otro desafío ha sido garantizar la calibración y verificación cuidadosa de la herramienta. Para ello el equipo ha utilizado datos reales y precisos de consumo de gas natural y electricidad provenientes de operaciones de tratamiento térmico, cortesía de los miembros del PHTC, con el fin de verificar el consumo energético predicho por el modelo a temperaturas de operación definidas del horno.
Eventualmente alguna versión de esta herramienta estará disponible para usuarios fuera del consorcio. Sin embargo, actualmente, los miembros del PHTC se encuentran a la vanguardia tanto del desarrollo como del uso. Los investigadores enfatizaron este punto: “Esta herramienta es particularmente oportuna y esencial para la industria, ya que las empresas enfrentan una creciente expectativa de proporcionar reportes de emisiones transparentes y precisos”.
Si bien el mundo de las normas y regulaciones puede sentirse como un campo minado, las discusiones comparativas sobre esta herramienta revelan aplicaciones prometedoras a corto plazo para los especialistas en tratamiento térmico con operaciones internas.
