Machine Learning-based Investigation of the Influencing Factors on the Hardness of Steel Materials

Abstract

Abstract The hardness of steel is often regarded as a crucial indicator of its production and application. The influences of chemical composition and process parameters on the hardness of steel exhibit strong non-linearity, making it challenging to accurately predict hardness through traditional multivariate regression or orthogonal experiments. Although machine learning has achieved distinguished success in diverse applications, its use in studying Metal materials has emerged only recently. Inspired by Sunčana et al.’s work, we select the Jominy distance as an input variable for the chemical composition, and instead of his subtly finding the best artificial neural networks, we simply use Generalized Regression Neural Network (GRNN) and due to the heterogeneity of the data, a second-order clustering method is employed as a data pre-processing step. The predicted hardness values were obtained using leave-one-out cross-validation, and the optimal smoothing factor (spread) was selected based on the Root Mean Square Error (RMSE) criterion. The optimal prediction results showed that in Configuration I, the RMSEs of the two types of data were 62.41 HV and 20.51 HV, respectively, while in Configuration II, they were 66.38 HV and 29.51 HV. Results showed Configuration I was more successful than Configuration II. This research presents a novel approach for predicting the hardness of metal materials using GRNN which is designed to address the need for quick adaptation to changes in design methods and the increasing demand for high-quality manufactured products.