Predictive Modeling of Hardness Values and Phase Fraction Percentages in Micro-Alloyed Steel during Heat Treatment Using AI

Abstract

In this work, we have proposed an AI-based model that can simultaneously predict the hardness and phase fraction percentages of micro-alloyed steel with a predefined chemical composition and thermomechanical processing conditions. Specifically, the model uses a feed-forward neural network enhanced by the ensemble method. The model has been trained on experimental data derived from continuous cooling transformation (CCT) diagrams of 39 different steels. The inputs to the model include a cooling profile defined by a set of time-temperature values and the chemical composition of the steel. Sensitivity analysis was performed on the validated model to understand the impact of key input variables, including individual alloys and the thermomechanical processing conditions. This analysis, which measures the variability in output in response to changes in a specific input variable, showed excellent agreement with experimental data and the trends in the literature. Thus, our model not only predicts steel properties under varied cooling conditions but also aligns with existing theoretical knowledge and experimental data.

Digital Model of Plan View Pattern Control for Plate Mills Based on Machine Vision and the DBO-RBF Algorithm

“…The method was developed for the horizontal stand drives of a 5000 mm plate mill and is supported by numerical examples which have been applied to the development of an observer of the elastic torque of the rolling stand's electromechanical system. Bassi, A et al [23] developed a predictive model using a feed-forward neural network to determine the hardness values and phase fraction percentages of steel during heat treatment under specific cooling conditions. Their study enhanced the quality and performance of the resulting product.…”