Mechanical Property Prediction of Industrial Low-Carbon Hot-Rolled Steels Using Artificial Neural Networks

Abstract

This study investigated the application of neural network techniques to predict the mechanical properties of low-carbon hot-rolled steel strips using industrial data. A feedforward neural network (FFNN) model was developed to predict the yield strength (YS), ultimate tensile strength (UTS), and elongation (%EL) based on the chemical composition and processing parameters. For the low-carbon hot-rolled steel strip (C: 0.02-0.06%, Mn: 0.17-0.38%), 435 datasets were utilized with 17 input parameters, including 15 composition elements, finish rolling temperature (FRT), and coil target temperature (CTT). The model was trained using 335 datasets and tested using 100 randomly selected datasets. The optimum network architecture consisted of two hidden layers with 34 neurons each, achieving a mean squared error of 0.014 after 200,000 iterations. The model predictions showed excellent agreement with the actual values, with mean percentage errors of 4.44%, 3.54%, and 4.84% for the YS, UTS, and %EL, respectively. The study further examined the influence of FRT and CTT on mechanical properties, demonstrating that FRT has more complex effects on mechanical properties than CTT. The model successfully predicted property variations with different processing parameters, thereby providing a valuable tool for alloy design and process optimization in steel manufacturing.