Predicting the Cooling Rate in Steel-Part Heat Treatment via Random Forests

Heat treatment is a thermal-processing method involving controlled heating and cooling cycles designed to achieve the desired properties of materials. Among these steps, the cooling rate in heat treatment plays a crucial role, as it significantly influences the resulting material properties. In this paper, we investigated the feasibility of random forests in estimating the cooling-rate parameters for the steel-part heat treatment process. Random forests are particularly appealing in modeling an ensemble of expressive decision trees from which cooling can be modeled and estimated from the interaction of metal features. Our computational experiments using real-world data from industrial-scale operations demonstrated the advantageous properties of random forest regression models, particularly when combined with a random oversampling scheme. We also found that the chemical composition—specifically carbon and chromium content—as well as the weight of the steel parts, are key features that predict the cooling rate of steel parts. Furthermore, our validation using real-world cooling scenarios aligned closely with the practical insights of seasoned operators who routinely recommend cooling parameters for the metal-normalizing process. Our results highlight the effectiveness of the ensemble approach of random forest for practical applicability in industrial-scale heat treatment.