Predicting the Hall-Petch slope of magnesium alloys by machine learning  

作　　者：Bo Guan Chao Chen Yunchang Xin Jing Xu Bo Feng Xiaoxu Huang Qing Liu 

机构地区：[1]Institute of Applied Physics,Jiangxi Academy of Sciences,Nanchang,330029,China [2]Key Laboratory for Light-weight Materials,Nanjing Tech University,Nanjing 210009,China [3]International Joint Laboratory for Light Alloys,College of Materials Science and Engineering,Chongqing University,Chongqing 400030,China [4]Institute of New Materials,Guangdong Academy of Sciences,Guangzhou,510650,China

出　　处：《Journal of Magnesium and Alloys》2024年第11期4436-4442,共7页镁合金学报(英文)

基　　金：supported by Jiangxi Provincial Natural Science Foundation (No.20224BAB214017);Jiangxi Academy of Sciences(Nos.2022YSBG22023,2022YSBG22024,2022YYB25,2022YYB26,2022YSBG10001);financially National Natural Science Foundation of China (Nos.52071039 and 51871032);Natural Science Foundation of Jiangsu Province (No.BK20202010)。

摘　　要：Hall-Petch slope(k) is an important material parameter, while there is a great challenge to accurately predict the k value of magnesium alloys due to a high dependence of k on the material parameters, deformation history and testing conditions. The present study demonstrates that machine learning could provide opportunities to overcome this challenge. Two machine learning models, artificial neural network(ANN)and random forest(RF), were built and validated using 138 data. The results showed that increasing the training data set would enhance the prediction efficiency of both models. Comparing to the RF model, the ANN model showed higher accuracy. The correlations between individual attribute and k values were also discussed.

关 键 词：Hall-Petch slope Mg alloys Grain boundary strengthening Machine learning 

分 类 号：TG146.22[一般工业技术—材料科学与工程] TP181[金属学及工艺—金属材料]