Prediction and Analysis of Tensile Properties of Austenitic Stainless Steel Using Artificial Neural Network
Yuxuan Wang, Xuebang Wu, Xiangyan Li, Z.M. Xie, Rui Liu, Wei Liu, Yange Zhang, Yichun Xu, C.S. Liu
Abstract
Predicting mechanical properties of metals from big data is of great importance to materials engineering. The present work aims at applying artificial neural network (ANN) models to predict the tensile properties including yield strength (YS) and ultimate tensile strength (UTS) on austenitic stainless steel as a function of chemical composition, heat treatment and test temperature. The developed models have good prediction performance for YS and UTS, with R values over 0.93. The models were also tested to verify the reliability and accuracy in the context of metallurgical principles and other data published in the literature. In addition, the mean impact value analysis was conducted to quantitatively examine the relative significance of each input variable for the improvement of prediction performance. The trained models can be used as a guideline for the preparation and development of new austenitic stainless steels with the required tensile properties.