Machine learning - assisted prediction of yield strength in irradiated type 316 stainless steels

• Published in: Fusion engineering and design Vol. 208; p. 114691
• Main Authors: Wang, Ziqiang, Yang, Chen, Gao, Ning, Wu, Xuebang, Yao, Zhongwen
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2024
• Subjects: Irradiation hardening; Machine learning; Prediction; Type 316 stainless steels; Yield strength; Yield strength; Type 316 stainless steels; Irradiation hardening; Machine learning; Prediction
• ISSN: 0920-3796
• DOI: 10.1016/j.fusengdes.2024.114691

•A machine learning model for predicting yield strength in irradiated type 316 stainless steels is developed.•The gradient boosting model exhibits superior prediction performance and stability.•The results from the machine learning model are in reasonable agreement with the conventional understanding. The investigation into irradiation hardening and embrittlement is of critical importance in nuclear material subject. This work explores the applications of machine learning (ML) to predict the yield strength of irradiated type 316 stainless steels. A dataset comprising 354 samples is compiled through an extensive review of prior experimental studies. Each sample has 23 potentially influential features. Five distinct machine learning models are trained and evaluated. Among these models, the Gradient Boosting (GB) model demonstrates superior prediction performance and robust stability. The prominent factors identified by the GB model are in reasonable agreement with established knowledge regarding the determinants of yield strength in irradiated type 316 stainless steels. These findings provide critical insights into the mechanical properties of irradiated type 316 stainless steels.