FOUNDRY CASTING TECHNICAL CONTENT Archives

Refractory Insulation: The Quiet Defender of Furnace Longevity

When it comes to furnace linings, most heat treaters focus on the hot-face materials, the heavy-duty refractories taking the brunt of molten metal, corrosive slags, and extreme heat. And just behind that armor lies a quiet defender: the refractory insulation layer. This layer is often the last line of defense between a functioning furnace and a costly, catastrophic failure. In this Technical Tuesday installment, Roger M. Smith, director of technical services for Plibrico Company, LLC helps readers understand the valuable role of refractory insulation for thermal stability.

This informative piece was first released in Heat Treat Today’s October 2025 Ferrous & Nonferrous Heat Treatments/Mill Processing print edition.

Why Refractory Insulation Matters

Refractory insulation is more than a buffer or a back-up. It provides structural support to the working lining, maintains shell temperatures within safe limits, and cushions the entire structure against the stresses of expansion and contraction. When this layer fails, you don’t just lose insulation, you risk cracks, shell overheating, and lining collapse. In other words, it can turn a maintenance project into a full-blown emergency.

The Strength Factor: Why Compressive Strength Counts

If there’s one property that deserves special attention, it’s compressive strength. The insulation layer is like the foundation of a house: if it cannot support the load above it, the whole structure suffers. Insufficient compressive strength can lead to creeping, crushing, and distortion, all of which compromise the stability of the hot-face refractory.

At green or ambient conditions, most types of insulating refractories, including monolithic, mineral wool, and ceramic fiber boards, exhibit similar compressive resistance, typically in the range of 40−50 psi at 10% deformation, but strength changes significantly once the furnace heats up.

For example, most mineral wool and ceramic fiber boards contain organic binders that burn off at around 475°F, reducing their compressive strength by roughly 50% at furnace operating temperatures (based on the board manufacturer’s technical data sheets, see Table A). Over time, this can increase thermal conductivity through the reduced thickness of the insulating layer.

In contrast, monolithic lightweight insulating castables, like Plibrico’s Plicast Airlite 25 C/G, not only retain their compressive integrity as the temperatures rise, but they actually gain strength, according to ASTM C165 test data as the material fully sets and stabilizes under heat.

Figure 1. Monolithic insulation, gunned in place, stays strong and gains compressive strength during heat-up.

This difference matters: compressive strength is not static. It changes as the material heats up and insulating products that hold their strength at service temperatures provide a more stable, safe, and reliable support for the hot-face lining.

The takeaway? Stronger, more stable insulation is not just filler. It’s an active structural layer that helps prevent hot-face sagging, cracking, and premature failure, directly contributing to longer furnace life.

Thermal Stability: More Than Just Heat Resistance

Figure 2. Confirmed anchor layout and fully prepped furnace wall ready for monolithic insulation installation.

Compressive strength plays a direct role in thermal stability. Denser, stronger castables with lower porosity are far better at resisting gas penetration, chemical attack, and erosion than lightweight, weaker alternatives.

When insulation loses stability, it can create voids, cracks, and hot spots, risks that threaten not only the hot-face layer but the furnace shell itself. This is why density and porosity are critical: denser insulating castables maintain their structure under load, resist infiltration, and provide reliable support for the hot face.

By contrast, mineral wool and board products often weaken as their organic binders burn off at service temperatures, leading to deformation and unpredictable thermal gradients.

Monolithic lightweight insulating castables can offer a more robust alternative. They retain their integrity as temperatures climb and can even gain compressive strength as they fully set and sinter during heat-up. This added stability reinforces the hot-face layer and helps prevent failures during thermal cycling.

There’s another layer to this: long-term thermal cycling. Furnaces rarely stay at one steady temperature; they ramp up, cool down, and undergo countless micro-cycles during operation. Insulation that can absorb these changes without cracking or delaminating is critical for avoiding premature lining failures.

In short, thermal stability is structural stability — the better your insulation performs under heat and cycling, the longer your furnace lining will last.

Designing for Expansion: Building Flexibility Into the System

Here’s where many lining failures start: in the different layers of lining expanding at different rates.

Figure 3. Completed furnace wall insulation installation, finished in half the time required for board installation.

Hot-face refractories, often dense high-alumina castables, have significantly different thermal expansion coefficients compared to the lighter, more porous insulating castables behind them. The hot face may swell aggressively under load, while the insulation expands far less. If those differences are not accounted for, the result is tensile stresses, delamination, and cracking at the interface between layers. Over time, those cracks can grow, creating pathways for heat and corrosive agents to reach deeper into the lining.

This is where thoughtful design makes all the difference:

Anchor systems must hold both layers securely but flexibly, allowing each to expand without transferring destructive stresses. Using materials like monolithic refractories adds another advantage: their insulating properties help better protect the base of refractory anchors, reducing localized heat buildup and minimizing stress concentrations that can lead to cracking or premature anchor failure.
Installation sequencing should avoid locking the hot-face layer too tightly to the insulation, preventing “shear failure” during heat-up.
Layer composition must be selected so the expansion mismatch is minimized, balancing mechanical stability with thermal shock resistance.

When expansion is designed for, rather than ignored, the entire lining behaves like a single, flexible system instead of two incompatible parts competing for space.

Practical Tips for Getting It Right

Prioritize compressive strength. Choose insulation with enough strength to support the hot-face lining under load. Materials like monolithic lightweight insulating castables maintain or even increase compressive integrity at service temperatures, improving overall lining stability.

Figure 4. Gunned to the ideal thickness, insulation built with compressive strength can handle stress, prevent cracking, and maintain shape under heat.

Pick the right material for the zone. Not every insulating castable is created equal. Match density, chemistry, and expansion to the application.

Control the install. Low-water mixes, vibration placement, and proper curing are non-negotiables if you want consistent density and strength.

Don’t skip the heat-up schedule. Rushing dry-out or startup is one of the fastest ways to ruin a lining before it is even in service.

Revisit your anchor design and how you install around it. Poorly designed anchor layouts can lead to stress points and premature lining failures, so reviewing and optimizing the design is one of the cheapest ways to prevent costly mechanical issues. Plus, consider the installation method: board insulation requires time-consuming cutting and fitting around each anchor, while monolithic insulating refractories like Plicast Airlite 25 C/G can be installed around anchors in less than half the time, reducing labor while improving performance.

Built to Last

Insulation is not just about slowing heat loss, it is also about standing firm when your furnace is under its heaviest load. The right refractory insulation, engineered with compressive strength as a priority, gives your lining the backbone to absorb mechanical stresses, resist cracking, and maintain its shape through the punishing cycles of heat-up and cool-down. It does not just protect the shell; it supports the hot face, prevents hot spots, and preserves the entire system’s structural integrity.

Compression Recovery Data

Product	Temperature (ºF)	Compressive Resistance at 10% Deformation (psi)
Monolithic Insulation Lightweight Castable/Gunite (Plibrico Plicast Airlite 25 C/G)	230	40
	650	41
	1000	47
Mineral Wool Board (published)	ambient	38
Ceramic Fiber Board (published)	ambient	50
Ceramic Fiber Board (published)	2000	23

3rd Party Test Lab – Orton Labs
ASTM C165 Measuring Compressive Properties of Thermal Insulations

Note: Mineral Wool and Ceramic Fiber Boards contain organic binders that burn off by 475ºF. This reduces the strength of the board by 50% along with decreasing other important properties including thermal conductivity.
Table A. Compressive strength at service temperatures: comparison of compressive resistance (10% deformation) between monolithic insulation lightweight castable/gunite materials such as Plibrico’s Plicast Airlite 25 C/G and mineral wool and ceramic fiber boards at increasing temperatures.

Choosing monolithic insulating castables that gain strength at operating temperatures, instead of mineral wool or ceramic fiber boards that lose half their capacity as binders burn away, is an investment in lining longevity. Get this layer right, and you secure longer campaigns, lower maintenance costs, and the confidence that your furnace can keep pace with production demands. Get it wrong, and you risk premature failures, costly outages, and avoidable downtime. In the end, refractory insulation built for compressive strength and stability is not just a detail, it is what keeps your furnace, and your operation, running at its best.

About The Author:

Roger Smith
Director, Technical Services
Plibrico

Roger Smith is a seasoned professional in the refractory industry. With Master of Science in Ceramic Engineering from the University of Missouri – Rolla, Roger has over 15 years of experience in the processing, development, and quality assurance of both traditional and advanced ceramics. He has a proven track record in developing innovative ceramic formulations, scaling up processes for commercial production, and optimizing manufacturing operations.

For more information: Visit www.plibrico.com.

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Applied Machine Learning and Optimization in Steel Melting

April 29, 2025 / FOUNDRY CASTING TECHNICAL CONTENT

What data can be gleaned for optimizing charge combinations? Check out this Technical Tuesday installment, by Tim Kaufmann and Dierk Hartmann, of Hochschule für angewandte Wissenschaften Kempten; Shikun Chen and Johannes Gottschling, of Universität Duisburg-Essen.

This article showcases the utilization of data-driven modeling assisted by machine learning (ML) to simulate the melting process of different steel grades in a medium-frequency induction furnace concerning the target variable of energy consumption. The results of the predictive models developed are presented in this article, along with the possibility of producing optimized charge combinations through the use of predictive outcomes and backward analysis.

This informative piece was first released in Heat Treat Today’s April 2025 Annual Induction Heating & Melting print edition.

Introduction

The steel and foundry industry faces several ongoing challenges, such as escalating costs of raw materials and energy, CO₂ emission regulations, and fierce global competition. To tackle these challenges, there is a continual demand for improving current production processes. Despite the strides made by smelters and equipment manufacturers in enhancing plant technology, there is still room for improvement in production procedures and processes. One possible approach to enhancing these processes is through modeling.

In recent times, digitalization and machine learning (ML) have emerged as a promising modeling method, as demonstrated in material development or process optimization in the steel industry (Lee et al., “A Machine-Learning-Based Alloy,” 11012; Klanke et al., “Advanced Data-Driven Prediction,” 1307-1313; Yingjun et al., “A Machine Learning and Genetic,” 360-375.) The subsequent example showcases how the melting process in a medium-frequency induction furnace can be modeled with respect to energy consumption, using specific process data obtained from a steel foundry, and subsequently optimized through synthetic data generation and backward analysis.

Modeling

Various ML algorithms [including Random Forest, Extra Trees, LightGBM, XGBoost, MLP (Neural Network), K-Nearest Neighbors] were trained and their hyperparameters optimized for modeling Erikson et al., “Autogluon-tabular”. The hyperparameters are set before the learning process begins and influence how well a model can represent a process. Some examples of hyperparameters in ML are the learning rates, the number of hidden layers in a deep learning neural network, or the number of branches in a decision tree.

For the modeling, data from the process, induction furnace, and charge database of a medium-sized steel foundry were consolidated and pre-processed. The melting unit is a medium-frequency induction furnace with a capacity of approximately 7 tons. Figure 1 depicts an overview of the modeling workflow. The process and furnace databases contain data, such as the chemical analysis of the input raw materials, the measured melting time, and the measured melting energy requirement of the respective melts. The required melting energy (kWh), the required melting time (min) to reach the required tapping temperature, and the alloy quantities (Co, FeCr, FeV, FeSi, etc. kg) to be added after the melting process to correct the chemical analysis were selected as target values. The charge database contains data on the charge scrap for each melt, with details of its quantity and chemical analysis.

At the foundry, the scrap is roughly pre-sorted in separate bins so that the scrap used in a batch can be distinguished. As an intermediate step, the expected melting enthalpy of the scrap was calculated from the chemical analyses using simulation software (CALPHAD method). Based on the thermodynamic data, an ML model was trained to also consider the theoretically required melting enthalpy of the raw materials based on the chemical analysis. The measured raw data consist of approximately 10,000 individual melts of different steel grades. After pre-processing (removing outliers, formatting the data, etc.) and applying domain knowledge, about 70% of the data was used for process modeling. Domain knowledge in this context means reviewing and filtering the data with an understanding of the process. For example, some obvious outliers or erroneous data, such as negative charge quantities or charges of allegedly more than 7 tons of material, were not detected by the pre-processing algorithms and were manually filtered out.

Figure 1. Process modeling of the melting process *The ML Models are a function of the change of energy, melting time, and chemical composition of the ferro alloys

The remaining dataset contains information on approximately 7,000 individual steel melts with approximately 300 influencing variables (columns in the dataset influencing variables, also called “features”) of which approximately 200 are charge-relevant influencing variables. In addition, the steel scrap was divided into groups, such as recycled or foreign scrap and alloys, characterized based on its empirically assessed geometry (cut-offs, “bones,” plates, chipped scrap, etc.), and added to the dataset as information.

Table 1. Overview of the prediction metrics of the energy models for 1.2379 steel

The data were subsequently split as 70% training, 20% validation, and 10% test data. The algorithms were trained on the training data and then tested for prediction quality on the test data. This step is necessary because overfitting or underfitting can occur when training ML algorithms. This means that the models perform well on the training data but poorly on unknown future process data. The test data, representing completely unknown future process data or states, are separated beforehand. In each case between the prediction of the model and the “real” measured value of the respective melt in the test dataset, the prediction of the models was evaluated with the metrics MAE (mean absolute error), MSE (mean square error), RMSE (root mean square error), and R² (coefficient of determination).

The energy consumption during melting in an induction furnace depends on many factors, such as the raw materials being fed (fine or bulky material, impurities, etc.), the sequence in which they are charged, the actual process control (e.g., is the furnace lid open for an unnecessarily long time due to bulky scrap in the charge), and many other direct and indirect influencing factors. Due to this complexity, modeling

Figure 2. Result of the process model in terms of predicting the energy required to melt 1.2379 steel

Figure 3. Residuals of the energy model test data for 1.2379 steel

Figure 2 depicts the comparison of the values (1.2379 steel) of energy consumption (x-axis) predicted by the best ML model based on the input features and the actual measured values (y-axis) in the test and training datasets, respectively. For the training dataset the R² is 0.92 with an RMSE of 58 kWh, and for the test dataset, the R² is 0.69 with an RMSE of 119 kWh (Table 1). For the test data, this corresponds to a relative prediction error of about 5%–10%.

Figure 3 illustrates the residual distribution (difference between the actual measured value and the model prediction) of the 1.2379 energy model in the test dataset. The residual distribution gives an indication of the prediction quality of a model. If the expected value of the residuals is not close to zero and they are not approximately normally distributed, this means that the model has a systematic tendency to either over- or under-predict. Furthermore, if there is a pattern in the residuals, the model does not appear to be able to explain some relationships within the data and is therefore qualitatively inconsistent. In the generated model, the residuals are almost normally distributed, and the prediction error does not appear to follow any pattern, suggesting good prediction quality.

Charge Optimization

After a trained ML model has been prepared for use, backward analysis can be applied to the training dataset to determine the range of values of the independent variable that corresponds to a given target variable. Consequently, backward analysis offers an inverse function of the prediction function, which can be leveraged to determine optimized process values. In this scenario, the optimized process value is the charge composition, with the target variable being energy consumption.

There are multiple mathematical optimization methods that can be employed for this purpose based on a well-trained ML model. A straightforward and easy-to-understand approach is to create a dense set of independent process variables using linear interpolation within a given range, such as the minimum and maximum values of a variable. The target variable is subsequently predicted based on this set of generated variables. This method can be computationally intensive and time consuming, and it does not consider the hidden patterns within the dataset, resulting in some useful information being disregarded.

In order to capture the hidden information and accurately reflect the true value of the original data set, a deep learning-based method called SDV (Synthetic Data Vault) is used in this work Patki et al., “The Synthetic Data Vault,” 1-10. Various synthetic data generation algorithms, such as Gaussian copula, are used in the SDV library. Mathematically, a Gaussian copula is a distribution over the unit cube between 0 and 1 in dimensions generated by applying the probability integral transformation to a multivariate normal distribution over all real numbers (R). Intuitively, the Gaussian copula is a mathematical function that can describe the joint distribution of several random variables by analyzing the dependencies between their marginal distributions. It can learn the intrinsic information of the original dataset to generate new synthetic data that have the same format and statistical properties as the original dataset.

Figure 4. The KDE comparison of real and synthetic variable values of the target variable energy consumption

Since the SDV library learns probabilistic rules, most of the synthesized data is general. To improve the quality of the synthesized data, some technical constraints can be defined when generating the data. For example, constraints can be set so that the values of a column in the generated data set are always larger or smaller than another column. Figure 4 shows the comparison of the Kernel Density Estimation (KDE) of the target variable energy consumption in the real and synthetic data. The distributions are very similar. In the current production process, the SDV dataset can be used to quickly determine the values that best approximate the required quality according to the prediction for the independent variables. This selection can then be further optimized, for example, in terms of cost and energy efficiency.

Figure 5 illustrates the process of backward analysis and resulting optimization based on the target, the required chemical elements, and the amount of scrap that must be included in a melt to ensure the target composition. The aim of data-driven optimization is to determine the most cost-effective scrap mix or “recipe,” taking into account the predicted energy consumption and metal yield. The database, which contains information from scrap suppliers, is constantly updated and fed new data. Because of this, the results of the optimization are automatically adjusted on an ongoing basis.

Figure 5. Sequence of backward analysis and large-scale charge optimization

The solution to the programming problem should indicate from which scrap supplier and which type of scrap combinations should be purchased to maintain the desired stock levels of the steel producer that will meet the above conditions (desired chemical analysis, minimized energy consumption, post-gating with ferro-alloys) or minimize costs Goutam and Fourer, “A Survey of Mathematical Programming,” 387-400.

The availability of individual scrap suppliers, the market price, and the levels of elements, such as chromium, vanadium, sulfur, and phosphorus, all affect how economically recovered steel scrap can be used. To ensure that steel grades are consistent throughout the cast or semi-finished product and to meet a client’s criteria, such as weldability and hardness, it is critical to control the amount of these elements in the final melt.

A model-based linear first-order (LP) optimization problem has been developed as a tool for scrap purchasing decision makers Applegate et al., “Practical Large-Scale,” 20243-20257 and Miletic et al., “Model-Based Optimization,” 263-266. The computations are performed by the model using the results of the ML model shown earlier in terms of energy consumption and scrap quality data, market prices, and supplier availability information. Prices, quality, and supplier information are included in the model along with quality and density constraints and a production schedule. The LP considers the following convex quadratic programming problem:

where A is an m × n Matrix and Q is a symmetric and n × n positive semidefinite matrix. The vectors of the input features have the upper bounds u^c and u^v and the lower bounds l^c and l^v , which have values in R U+∞ and in R U–∞ respectively. This equation assumes that l^c ≤ u^c and l^v ≤ u^v. For example, it can take into account that a particular scrap is only used between 500 kg and 1,500 kg, which may be due to process-related circumstances, and is therefore added as a constraint to the optimization objective.

An approximate linear cost equation is used in the model. Scrap costs are determined by market prices and availability, as well as internal storage costs; energy costs are calculated by estimating the energy consumption predicted by the ML model for each type of scrap and the amount of electricity required. The work described above ends with a web-based user interface (Figure 6) that displays the concrete purchase plan. Transportation of the purchased scrap could be considered through route planning. Finally, an estimate of the quantitative carbon footprint of the melting process and the logistics of scrap delivery can be calculated and tracked.

Figure 6. Overview of the entire software: In the background, the software accesses the generated ML model and the database of available scrap and generates an optimized shopping list for the scrap. Factors such as energy consumption predicted by the ML model, scrap prices, calculated CO2 emissions, the distance, and desired chemical composition are considered.

Conclusion

This article has shown how the melting and purchasing process in the steel industry can be modeled and optimized using modern methods from the field of artificial intelligence and mathematical optimization methods. Based on the input features (the scrap composition and the process parameters), the generated models can predict the expected energy consumption with a relative error of about 5%–10%. The optimization software can then be used to generate a scrap composition. Here, the composition of the purchase list from a purely monetary point of view (scrap prices) is supplemented by the consideration of resource and energy efficiency.

The foundry and steel industry naturally must go through a large number of (partial) processes on the way from raw material to finished casting or semi-finished product, where a large amount of production data is generated. For a future data-driven optimization of foundry processes, it is therefore necessary to consolidate this data to make the available knowledge usable for process optimization with the help of tools such as ML. This can provide foundries and their staff with another useful tool, like casting simulation, to further improve existing processes and procedures.

References

Applegate, David, Mateo Díaz, Oliver Hinder, Haihao Lu, Miles Lubin, Brendan O’Donoghue, Warren Schudy. “Practical Large-Scale Linear Programming Using Primal-Dual Hybrid Gradient.” Advances in Neural Information Processing Systems 34 (2021): 20243-20257.

Dutta, Goutam, and Robert Fourer. “A Survey of Mathematical Programming Applications in Integrated Steel Plants.” Manufacturing & Service Operations Management 3, no. 4 (2001): 387-400.

Erickson, Nick, et al. “Autogluon-tabular: Robust and accurate automl for structured data.” arXiv preprint arXiv:2003.06505 (2020).

Klanke, Stefan, Mike Löpke, Norbert Uebber, and Hans-Jürgen Odentha. “Advanced Data-Driven Prediction Models for BOF Endpoint Detection.” Association for Iron & Steel Technology Proceedings (2017), 1307-1313.

Lee, Jin-Woong, Chaewon Park, Byung Do Lee, Joonseo Park, Nam Hoon Goo, and Kee-Sun Sohn. “A Machine-Learning-Based Alloy Design Platform Th at Enables Both Forward and Inverse Predictions for Thermo-Mechanically Controlled Processed (TMCP) Steel Alloys.” Scientific Reports 11, no. 1 (2021): 11012.

Miletic, I., R. Garbaty, S. Waterfall, M. Mathewson. “Model-Based Optimization of Scrap Steel Purchasing.” IFAC Proceedings 40, no. 11 (2007): 263-266.

Patki, Neha, Roy Wedge, and Kalyan Veeramachaneni. “Th e Synthetic Data Vault.” IEEE International Conference on Data Science and Advanced Analytics (DSAA) (2016): 1-10.

Yingjun Ji, Shixin Liu, Mengchu Zhou, Ziyan Zhao, Xiwang Guo, and Liang Qi. “A Machine Learning and Genetic Algorithm-Based Method for Predicting Width Deviation of Hot-Rolled Strip in Steel Production Systems.” Information Sciences 589 (2022): 360-375.

This article content is used with permission by Heat Treat Today’s media partner heat processing, which published this article in February 2023.

About The Authors:

For more information:
Contact Tim Kaufmann at tim.kaufmann@hs-kempten.de
Dierk Hartmann at dierk.hartmann@hs-kempten.de
Shikun Chen at shikun.chen@uni-due.de or
Johannes Gottschling at Johannes.gottschling@uni-due.de

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Forging and Metalcasting Resources To Keep You Informed

January 31, 2023 / FEATURED NEWS, FORGING TECHNICAL CONTENT, FOUNDRY CASTING TECHNICAL CONTENT, MANUFACTURING HEAT TREAT

We've assembled some of Heat Treat Today's resources on forging and metalcasting. Read or listen to what the experts have to say on these important topics in the heat treat industry.

This Technical Tuesday original content piece will help you wade into an introduction of these heat treatment processes. Follow the links to dive deeper into the studies.

The span of articles, radio episodes, and TV clips below are compiled to learn more about forging and casting. Heat treating is developing and changing through the years, and it's wise to keep swimming with the current of information.

Simulating Induction Heating for Forging

What can simulation software do for you? Manufacturers are able to run the software to act upon the steel billet prior to forging. Read more about the process here. The simulation shows results in the metal to help the user best plan for desired results. One of the decisions that can be helped is, "the selection of right forging temperatures for plain carbon and alloy steels to avoid possible damage by incipient melting or overheating."

A Look at Steel and Iron

Dan Herring
"The Heat Treat Doctor"
The HERRING GROUP, Inc.

Read or listen to this episode of Heat Treat Radio with expert Dan Herring who discusses metals such as stainless steel, tool steel, cast iron, high and low carbon steels, and more. He looks at their production and their uses.

"I wanted to set the stage for it to say that it’s the end-use application by the customer that fuels the type of steel being produced and fuels the form in which the steel is produced," says Herring.

Investment Casting in Turbine Blades

Take a look at how an alumina and silica (quartz) mix are improving metal casting for support rods used in aerospace manufacturing. "LEMA™, a range of proprietary alumina-based materials that provide double the mechanical strength of quartz while providing significantly improved leaching times, compared with typical high purity alumina," provides many benefits for metal casting. Jump into this piece to find out more about this metal casting example.

Direct From the Forge Intensive Quenching

President
Akron Steel Treating Co & Integrated Heat Treating Solutions, LLC

In this discussion, expert Joe Powell says, "My thing is to develop a robust process that can be applied and implemented using automation and new equipment with the proper pumps and material handling that is all integrated into a seamless process." He plunges in to talking about immediate quenching pieces in water after heat treating and what they are learning at the forge shop.

Heat Treat TV

Here are a few episodes to keep you afloat while moving into deeper waters.

Click on these two illustrations to watch the full episodes.

Search for heat treat services and products on Heat Treat Buyers Guide.com

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Heat Treat Radio #80: Lunch & Learn with Heat Treat Today – Mill Processes and Production, part 2

August 25, 2022 / ALUMINUM PROCESSING TECHNICAL CONTENT, FEATURED NEWS, FOUNDRY CASTING TECHNICAL CONTENT, HEAT TREAT RADIO, MANUFACTURING HEAT TREAT

Heat Treat Radio host, Doug Glenn, and several other Heat Treat Today team members sit down with long-time industry expert Dan Herring, The Heat Treat Doctor® of the HERRING GROUP, to finish the conversation about mill processes and production. Enjoy this third informative Lunch & Learn with Heat Treat Today.

Below, you can watch the video, listen to the podcast by clicking on the audio play button, or read an edited transcript.

HTT · Heat Treat Today – Mill Processes and Production, Part 2" href="https://soundcloud.com/htt-614286209/heat-treat-radio-80-lunch-learn-with-heat-treat-today-mill-processes-and-production-part-2" target="_blank" rel="noopener">Heat Treat Radio #80: Lunch & Learn with Heat Treat Today – Mill Processes and Production, Part 2

The following transcript has been edited for your reading enjoyment.

Dan Herring (DH): When it comes to heat treating, the mill will do what we typically call ‘basic operations.’ They will anneal the material and, if you’ll recall, annealing is a softening operation (it does other things, but we will consider it, for the purpose of this discussion, a softening operation) so that the steel you order from the mill will be in a form that you can then manufacture a product from. You can machine it, you can drill it, you can bend it and things of this nature.

There are various forms and various types of steel that can be ordered directly from the mill. So, the mill typically does annealing operations and normalizing operations. The difference between annealing and normalizing is that annealing has a slower cooling rate than normalizing does.

In the aluminum industry, we don’t talk about normalizing but talk about homogenizing. Homogenizing is to aluminum what normalizing is to steel; it’s a crude analogy, but it’s true. The mill can do other processes; they can do other heat treatments, they can do specialized rolling and things of this nature to give you enhanced mechanical properties. In today’s world, there is a lot of what we call “custom” or “specialty mills” that can manufacture very specialized products. There are mills that primarily make pipe and tube, there are mills that make primarily wire, there are mills that make primarily strip. There are some very customer-specialized mills out there. In general, a mill will produce most of the type of products that we see or use in industry (or the steel for those products), and they will make it in a form that is usable for the end user and heat treated to a condition where the end user can make a product with it. Now, obviously, once you make a product, you may then have to further heat treat that product, for example, to harden it or to give it certain characteristics that you need. We’ll talk about those things in later discussions about this.

What I did want to talk about is the types of steel that are produced by the mills. I’ll do this, hopefully, in a very, very broad context, but I think it will make sense to everybody. Again, metallurgists aren’t known too much for their creativity, so we start out with something called carbon steel. Very original. There is low carbon steel, medium carbon steel and high carbon steel. Low carbon steel has low carbon, medium carbon steel has medium carbon, and a high carbon steel has high carbon.

Now, to be more serious, a low carbon steel typically has less than or equal to 0.3% carbon, or less than 0.3% carbon. A medium carbon steel has between .3% carbon and .6% carbon, and a high carbon steel is greater than .6% carbon. An example of a medium carbon steel might be a 1050 or 1055 grade of steel. Those are commonly used for stampings, for example. So, all of your seatbelt, both the tongue and the receptacle are made of a 1050/1055 steel and they’re austempered to give them both strength and toughness so that in an accident, the buckle won’t shatter because it’s hard but brittle and it won’t bend abnormally and therefore release because it has inherent toughness.

So, there are various things you do with these carbon steels in the heat treat mill to enhance their properties. Carbon steels are used because they’re low cost and they’re produced in tremendous quantities. If you went to a hardware store and bought a piece of steel, it is very likely it will be a simple carbon steel.

On the other hand, we also make alloy steels and, interestingly enough, there are low alloy steels, medium alloy steels, and guess what, high alloy steels. Again, metallurgists are very creative with their names. But idea here is you get higher strength than a carbon steel, a little better wear resistance and toughness, you get a little better corrosion resistance, for example, you might even get some specialized electrical properties and things like this.

But low carbon steel, just to go back to that for a minute, as I said, is produced in huge quantities. Examples are steel for buildings, steel for bridges, steel for ships. We learned our lesson, by the way, with the Titanic; we got the steel right this time. The problem with that steel, by the way, was high in sulfur which embrittled it, interestingly enough, in cold water. So, when it hit the iceberg, the steel shattered because it was brittle because it had too much sulfur. But we learned our lesson.

There are also various construction materials; anything from a wire that’s used in fencing to automotive bodies to storage tanks to different devices.

When you get into medium carbon steels, because they have a little better strength and a little better wear resistance, you can use them for forgings, you can use them for high strength castings. So, in other words, if you’re producing gears or axles or crank shafts, you might want to consider a medium carbon steel, or seatbelt components as we talked about.

Then there is the family of high carbon steels. Again, they can be heat treated to give you extremely high hardness and strength. Now, they’re obviously more expensive than medium carbon or low carbon steels, but when you’re making knives and cutlery components, (knives and scissors, for example), when you’re making springs, when you’re making tools and dyes. Railroad wheels are another example of something that might be made out of a high carbon steel. As a result of this, the type of product that your company is producing, means that you’re going to order a certain type of steel that you can use to make your product and give it the longevity or the life that your customers are expecting.

One of the things about steel that differentiates it from aluminum: Aluminum has a very good strength to weight ratio. But so again does steel, but obviously the strength to weight ratio, the weight is specifically much more, from that standpoint. But we can take steels that we produce from the mill, and we can do processes like quench and temper them. If we do that, we can make things like pressure vessels, we can make the bodies of submarines, for example, we can make various pressurized containers and things.

Stainless steel pots
Source-Justus Menke at Unsplash.com

There are a lot of different things we can do with steels to enhance the products that we’re producing. Besides just low carbon steel or carbon steels and alloy steels, we then can go into the family of stainless steels, for example. Most people think of stainless steels as being corrosion resistant. I’ll warn you that not all stainless steels, however, are corrosion resistant; some of them can corrode in certain medias or chemicals, if you will. But with stainless steels, a good example of that is food processing containers or piping or things that will hold food or food products, and again, we can make with stainless steels a variety of different products. We can make different components for buildings, for example, or for trim components and things.

Besides stainless steels, of course, we can make tool steels. Now, tool steels represents a very, very high alloy steel. The alloying content of tool steels is typically 30 to maybe 50% alloying elements: molybdenum and vanadium and chromium and these types of materials. As a result, we can make a lot of dyes and we can make a lot of cutting tools, we can make taps and other devices that are used to machine other metals, if you will. So, tool steels have a lot of application.

But there are a lot of specialty steels that are made by the mills, as well. One example of that, that I like to talk about or think about, is spring steels because you can make various things like knives and scraper blades, putty knives, for example, besides cutlery knives. You can make reeds for musical instruments, the vibrating instruments in the orchestra, if you will. You can make springs and you can make tape measures, tapes and rules and things of this nature out of these various spring steels, if you will.

Depending on what your end-use application is, the bottom line here is that whatever your end-use application is, there is a particular type of steel that you should be using and there is a form of that steel that you can use. Again, those steels can be produced by a variety of different processes; they can be forged, they can be rolled, hot and cold rolled, again. And when I’m talking about hot rolling, I’m talking about temperatures in typically the 1800-degree Fahrenheit to 2200/2300-degree Fahrenheit range. When I talk about hot rolling, the metal is, indeed, hot, if you will.

By the way, roughly, iron will melt at around 2800 degrees Fahrenheit, just to give you a perspective on that, if you will.

The key to all this is that the form that is produced by the mill meets the needs of their customers and their customers’ applications. If you need a plate, for example, they will produce plate in various sizes and thicknesses.

Rolling direction
Source: Barnshaws Group

By the way, just a quick note, and this is for all the heat treaters out there: Be careful of the rolling direction in which the plate was produced. We have found that if you stamp or cut component parts out of a plate with the rolling direction, or transverse or across the rolling direction, you can get vastly different properties out of the products. It’s amazing that you can get tremendous distortion differences from heat treated products depending on the rolling direction. If you’re stamping or forming out of a plate, you’re transverse or in line with the rolling direction. Most people don’t even think of that. They take the plate, they move it into the stamping machine, and they could care less about the rolling direction. Then, when the poor heat treater does his heat treating and distorts all the parts, the man comes back and says, “What’s wrong?”

By the way, that little example took only nine years of my life to solve. We had some, what are called, "springs" that are the backing on a knife. When you open a knife blade, there is a member that it’s attached to called a spring. Those springs were distorting horribly after being oil-quenched in an interval quench furnace. It happened to be a conversation around the coffee machine where one of the guys made the comment that, “You know, it’s really funny, we never had problems with distortion until we got that new stamping machine in.” Low and behold, in investigating it, the old machine took the plate in one direction, the new machine had to take the plate in a different direction and it rotated. . . . End result.

So, I guess for everybody listening, the key to this is that no matter what the material is that’s being produced, we need to use it sometimes in its cast form, we need to use it sometimes in its finished forms, which again can be bar and sheet and plate and wire and tube and things of this nature. And to get those shapes, we need to do things like hot and cold rolling, we need to do forging, we need to do operations like piercing to actually produce rings and things of this nature. So, although I didn’t go all the details about that, there is a lot of information out there about it. I wanted to set the stage for it to say that it’s the end-use application by the customer that fuels the type of steel being produced and fuels the form in which the steel is produced.

Perhaps as a last comment, on my end anyway, at this point, is the fact that a mill is a business just like anyone else’s business. We’re always looking for ways to cut costs, (not cut corners, but reduce cost), and mills have found that in the old days — and the old days weren’t necessarily the “good old days” — a mill made everything; they made all types of steel, they made all types of shapes and forms. But today, a lot of mills are saying it’s not economical to produce that particular type of steel or that particular form of steel, so we’ll leave that steel production to someone else, and we’ll only concentrate on high volume production.

You know, it’s very producing steel, a typical heated steel (and people will probably correct me on this), is somewhere in the order to 330,000 pounds of steel. So, if you’re a small manufacturer and don’t happen to need 330,000 pounds of steel, you have to go to a distributor and, more or less, maybe compromise a little bit to get the steel that you need. But the mills are producing large quantities of steel and very specialty steel grades, in general, today.

Doug Glenn (DG): It’s essentially specialization of labor so it helps keep each individual mill’s cost down, but it doesn’t have the variety it used to.

Let’s open up for questions, really quick. I’ve got one if nobody has one, but I hope somebody else has one. So, fire away if you’ve got one.

Carbon steel gate valve
Source: Matmatch

Bethany Leone (BL): When you said that, Doug, my question jumped out of my head. I had 3 questions though but the ones I remember aren’t that important. One is — I recently visited an old blast furnace in Pittsburgh, Carrie Blast Furnaces; everybody should go, if you’re in the Pittsburgh area), so some of this sounds familiar. The second thing I was wondering is just how high can the carbon percentages go in carbon steels, .6%+, right?

DH: Yes, greater than .6%, and it’s not uncommon for carbon in various types of steels to go over 1%. It typically can go in certain tool steels and things higher than that. But one of the things that differentiates a steel from a cast iron is the percentage of carbon in the material. And carbon over 2% is considered a cast iron as opposed to a steel. Steel has a carbon percentage from .008 all the way up to 2%. That’s a great question and something to be aware of. When you buy a cast iron skillet, for example, you’re getting a material that has greater than 2% carbon in it.

BL: The other question I had is sort of more on the business end, if you know any of this, is- with the high energy that it takes to process iron, I imagine there have been efforts to try to reduce costs to produce energy that’s used to be a technology and innovation and especially right now with many people concerned with sustainability in those practices, are there ways that maybe even clients have influenced how businesses iron manufacturers in the iron manufacturing world have been trying to keep those environmental loads down, do you know?

DH: That’s a very intriguing question. I don’t have all the facts and information on it, but I’ll share a few things. As opposed to the production of aluminum, which is primarily using electricity, steel production uses typically natural gas. There were, in the old days, oil-fired equipment and things of this nature but today it’s typically gas-fired furnaces and things of this nature. Now, I have to be careful when I say that because some of the steel refining methods, (for example, the vacuum arc remelting furnaces and things of this nature), again, use carbon electrodes and use electricity, if you will, in the process. But essentially, what they’re trying to do is they’re trying to, for example, capture waste heat and reuse it to preheat different materials and processes and things of this nature, and they’re using methods that are trying to make the overall equipment more energy-friendly; if you will, better insulations, better fit of components than the old days when they didn’t care too much about if we got heat pouring out into the shop, we don’t care. Today, we really care about those things.

But steelmaking, again — for a different reason than aluminum — is a very energy intensive process; it uses a lot of energy to produce steel.

I’ll make a quick comment also, and I’m not saying this especially from anyone internationally who happens to be listening in to this: I’m not saying this is an “America only” comment, if you will, but in 1900, the largest industry, the largest company in the U.S. was U.S. Steel. United States Steel was the number one most profitable company in the country. If you think about it, throughout what would be the 20^th century, steel and steel production has fueled, if you will, the American economy. We’ve since transitioned to other more angelic materials, if I can use that phrase; I won’t define it. However, who do you think produces over 50% of the world’s steel today? Anyone want to guess?

DG: The U.S.?

DH: No! China. And where is the manufacturing growth taking place? So, the production of aluminum, the production of steel, fuels manufacturing is my message here.

Yes, there are environmental consequences, but I often use the phrase and, again, this is not intended to be insultive to any one country, but for all the recycling, for all the energy saving, for all the environmental progress we can make in the United States, if we could reduce coal consumption in China (and India, of course), it would have major, major impact on the environment. And that’s not having 100-year-old steel mills, like we have here in the U.S., will go a long way, if you will.

DG: I’m going to give you 30 seconds, Dan, to answer one more question, okay? Here’s the question: Aluminum doesn’t rust, most steels do. Why is that?

DH: In simple terms, because aluminum reforms an aluminum oxide on the surface and that oxide is impenetrable, virtually, to further oxidation, whereas iron produces an iron oxide on the surface in the form of rust, it flakes off and you can reoxidize the surface. Now, there are steels — core10 is an example — self-rusting steels, that once they rust, they don’t reoxidize, but that’s the basic difference, Doug, between them.

DG: Perfect, perfect.

Alright guys. Thank you very much, Dan. I appreciate it. We’re going to get you on deck for another one here pretty soon on another topic, but we appreciate your expertise.

DH: Always a pleasure and, as I’ve said, I’ve reduced 3,000 pages into 30 minutes so hopefully people that are interested will read up more on these processes.

DG: Yes. Appreciate it. Thank you!

For more information, contact:

Website: www.heat-treat-doctor.com

Doug Glenn
Publisher
Heat Treat Today

To find other Heat Treat Radio episodes, go to www.heattreattoday.com/radio .

.

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Have You Seen These 18 Heat Treat Technical Resources?

November 2, 2021 / BRAZING TECHNICAL CONTENT, CARBURIZING TECHNICAL CONTENT, FEATURED NEWS, FOUNDRY CASTING TECHNICAL CONTENT, HARDENING TECHNICAL CONTENT, HIPING TECHNICAL CONTENT, MANUFACTURING HEAT TREAT, NITRIDING TECHNICAL CONTENT, PARTS CLEANING TECHNICAL-CONTENT

Welcome to another Technical Tuesday for 18 hard-hitting resources to use at your heat treat shop. These include quick tables, data sets, and videos/downloadable reports covering a range of heat treat topics from case hardening and thermocouples to HIPing and powder metallurgy.

Defining Terms: Tables and Lists

Table #3 Suggested Tests and Frequencies for a Polymer Quench Solution (in article here)
Case Hardening Process Equipment Considerations (bottom of the article here)
Nitriding vs. FNC comparative table here
9 Industry 4.0 Terms You Should Know here
Table 1: Limits of Error Thermocouple Wire (in article here)
Table 2: Limits of Error Extension Grade Wire (in article here)
Thermocouple Color Code Chart (in article here)
International Thermocouple Lead Colors (in article here)

Free Downloadable Reports

FREE ebook—High Pressure Heat Treatment: HIP here
FREE ebook – On-site Hydrogen Generation here
Forging, Quenching, and Integrated Heat Treat: DFIQ Final Report here

Visual Resources

HISTORIC VIDEO: Aluminum Heat Treatment here
Two simulations of a moving billet through heating systems (in article here)
Fourier’s Law of Heat Conduction (in article here)
Webinar on Parts Washing (link to full webinar at the top of the review article here)
Materials 101 Series from Mega Mechatronics, Part 4, Heat Treatment/Hardening here
Heat Treat TV: Press-and-Sinter Powder Metallurgy here

BONUS: 39 Top Heat Treat Resources

Heat Treat Today is always on the hunt for cutting-edge heat treat technology, trends, and resources that will help our audience become better informed. To find the top resources being used in the industry, we asked your colleagues. Discover their go-to resources that help them to hone their skills in the 39 Top Heat Treat Resources on this page of the September print magazine.

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Quartz, Alumina Combine for Innovative Aerospace Castings

January 29, 2019 / AEROSPACE HEAT TREAT, AEROSPACE HEAT TREAT NEWS, FEATURED NEWS, FOUNDRY CASTING TECHNICAL CONTENT

A global materials engineering company which designs and manufactures a wide range of high specification products recently released an innovative new material for use in production of turbine engine blades that combines the best of two key materials to improve strength and processing time for the investment casting industry.

The new material, developed by Morgan Advanced Materials, is known as LEMA™. In this Technical Tuesday feature, Eric Larson, Director of Technology and Process Improvement at the Technical Ceramics Business of Morgan Advanced Materials, explains how LEMA™ combines the best aspects alumina and silica (quartz) to provide an effective solution for manufacturers. Content is compiled by Jennifer Kachala, Product Engineer at Morgan’s Technical Ceramic’s business.

Quartz and alumina – the best of both worlds for turbine engine blades

The commercial aerospace industry is on the cusp of significant technological change. High fuel prices, stricter regulations on emissions, and intense competition from low-cost carriers are all driving a quest for more efficient aero-engines and components, where even small advantages can drive major benefits.

Turbine blades are no exception, with a recent report by Market Research Future suggesting that the market for commercial aircraft turbine blades is set to grow at a CAGR of 6 percent by 2023.

Not only is the investment casting industry preparing to meet this demand, but it’s also looking to gain advantages in every aspect of manufacturing, including for the support rods used in the production of turbine engine blades. The two most commonly used materials to cast these are quartz (silica) and alumina.

Both have advantages – and weaknesses. Quartz is the traditional material of choice and has the benefit of being chemically weak and fast to leach, which both accelerates and simplifies production. On the other hand, it is mechanically quite weak which can lead to processing issues and defects during investment casting of difficult metals like super-alloys.

In contrast, alumina rods have about four times the mechanical strength of quartz and are acknowledged for their strength and load-bearing capabilities. However, alumina is so chemically strong it can take several days to fully leach out the material, resulting in longer production times.

While both appear to offer almost opposite properties, they share one common advantage: neither create trace elements which can cause contamination in the process and compromise the quality and performance of parts.

So, neither quartz nor alumina is the perfect material. But what if there was a way of combining the best properties of each to create something new?

The Making of LEMA™

This was the challenge Morgan Advanced Materials set for itself in 2015, resulting in LEMA™, a range of proprietary alumina-based materials that provide double the mechanical strength of quartz while providing significantly improved leaching times, compared with typical high purity alumina.

Like most new inventions, the solution was reached after significant experimentation. The challenge lay in combining two materials and finding the right balance – a complex task, especially as the materials in question were so different.

In search of an answer, Morgan’s laboratories started with a method borrowed from glass science where two distinct phase-separate materials can be used to improve mechanical properties such as toughness or to provide a leaching path through the chemically-weaker glass. In the end an alumina-silicate ceramic was created with a leaching path of silica across the grain boundaries. Particle size distribution and processing parameters were adjusted until the desired mechanical strength was achieved.

Following a period of extensive live testing and refinement, LEMA™ was first introduced to the market in 2017.

Turbocharged Leaching Times, No Loss of Strength

Combining the mechanical properties of alumina with the chemical weakness of quartz, LEMA™ exhibits many unique and valuable properties. It’s almost twice as strong as quartz, and it has a slightly lower thermal expansion coefficient than alumina, which can help with metal leakages sometimes encountered with alumina rods during casting. In addition, LEMA™ is made of pure materials to ensure that the material satisfies the demand for trace element certification.

LEMA™ “crumbles out” when flushed, making it easier to remove during the leaching process. Moreover, like-for-like LEMA™ 250 parts will experience approximately a 20 percent mass reduction after 20 hours (at 300°F [149°C]) and 185 psi). Under the same conditions, a comparable alumina part does not demonstrate any mass loss.

In addition to its advantageous chemical and mechanical properties, LEMA™ also delivers significant commercial benefits. It can reduce investment casting times in turbine engine blades by accelerating leaching by up to 20 percent, solving many of the delays and production challenges which have long been frustrating the global investment industry.

Importantly, as there is less need for autoclave time during the leaching process, manufacturers are spared some of the costly investment in additional equipment. Recognizing the benefits, the industry has already begun to embrace LEMA™; major aerospace manufacturers have used LEMA™ to achieve the desired quality while also reducing costs.

LEMA™ offers a powerful solution for the investment casting of turbine blades, just as the industry is facing an increased demand for these critical components. By bringing together the best aspects of both quartz and alumina, it doesn’t just represent the best of both worlds: it represents a major breakthrough for the industry.

Photo credit and caption: iStock / Jet engine turbine (3D xray blue transparent)

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Vacuum Assisted High Pressure Die Casting Technology Used in Manufacturing

October 27, 2016 / AUTOMOTIVE HEAT TREAT, AUTOMOTIVE HEAT TREAT NEWS, FEATURED NEWS, FOUNDRY CASTING NEWS, FOUNDRY CASTING TECHNICAL CONTENT

Source: La Metallurgia Italiana –

Primary aluminum-silicon-magnesium alloys are by far the most widely used type in the manufacturing of safety parts for the automotive industry, such as suspension components and wheels, due to their excellent castability and good mechanical properties which can be further improved by heat treatment: solution, followed by water quenching and artificial aging T6 or T7. The recent structural castings, being extremely thin walled (of the order of 2.5 mm) and of rather great dimensions usually require the

use of High Pressure Die Casting. These parts must be defect free and heat treatable to attain the requested properties, ductility and weldability being the most difficult to achieve. Vacuum must be applied as well as a combination of precautions relative to the die design, die lubrication, melt quality and shot profile have to be taken.

Vacuum Assisted High Pressure Die Casting Technology Used in Manufacturing Read More »

Direct-Chill Casting

June 28, 2016 / ALUMINUM PROCESSING TECHNICAL CONTENT, ANNEALING TECHNICAL CONTENT, FOUNDRY CASTING TECHNICAL CONTENT, MANUFACTURING HEAT TREAT, MANUFACTURING HEAT TREAT NEWS

Source: Total Materia

Direct-chill (DC) casting is currently the most common semi-continuous casting practice in non-ferrous metallurgy. The process is characterized by molten metal being fed through a bottomless water cooled mould where it is sufficiently solidified around the outer surface that it takes the shape of the mould and acquires sufficient mechanical strength to contain the molten core at the centre. As the ingot emerges from the mould, water impinges directly from the mould to the ingot surface (direct chill), falls over the cast surface and completes the solidification.

FOUNDRY CASTING TECHNICAL CONTENT