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’s August 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
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.
For more information: Contact Zach Menard at zach.menard@hrc-digital.com.
