文献类型：期刊文献

英文题名：Investigation on regression model for the force of small punch test using machine learning

作者：Zhong, Jiru[1];He, Zhuangzhuang[1];Guan, Kaishu[1];Jiang, Tao[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China

年份：2023

卷号：206

外文期刊名：INTERNATIONAL JOURNAL OF PRESSURE VESSELS AND PIPING

收录：;EI(收录号：20233014441546);WOS:【SCI-EXPANDED(收录号:WOS:001049489300001)】；

基金：This project is funded by the National MCF Energy R & D Program of China (2022YFE03120000) , the National Natural Science Foundation of China (52105146, 52205173) , the Innovation Centre of Nuclear Mate- rials Fund (ICNM-2022-ZH-02) , and the Natural Science Foundation of Sichuan Province of China (2023NSFSC0912) .

语种：英文

外文关键词：Small punch test; Machine learning; Correlation analysis; Binary linear regression model

摘要：The analytical solution for the force-deflection curve of the small punch test (SPT) is still a challenge. Machine learning is employed in the present study to establish a model for estimating SPT forces by using the strength of a material. The yield strength and ultimate tensile strength of materials and corresponding SPT force-deflection curves generated by finite element simulations are the training and testing data for machine learning. Pearson correlation analysis was performed first to measure the statistical association between the SPT force at a given deflection and the strength of materials. It was shown that a binary linear regression model was capable of correlating SPT forces to the yield strength and ultimate tensile strength of a material. The model parameters were determined after training, and then this model was tested by testing data. The accuracy of the regression model was verified by hypothetical materials, X70 steel and Incoloy 800H alloy. This study gives a new insight into the relationship between force responses of SPT and material properties, and can promote the development of novel approaches for determining material tensile properties using SPT.