Complex multiphase predicting of additive manufactured high entropy alloys based on data augmentation deep learning
Chao Zhou, Youzhi Zhang, Heyang Xin, Xiaomin Li, Xizhang Chen
Abstract
Additive Manufacturing (AM) can rapidly fabricate and produce customized, large-scale components, thereby fully exploiting High-Entropy Alloys (HEAs)' performance and industrial value—their broad potential for application in extreme service environments such as nuclear energy and aerospace. Because HEAs have a vast composition space, researchers can accurately map their mechanical properties by HEAs phases to reduce time-consuming and intensive experimental work. However, it is still a considerable challenge to predict HEAs phases accurately. And machine learning (ML) can predict the phases to accelerate the study of HEAs.To accurately predict the HEAs phases, the study propose deep learning (DL) algorithms that rely on AM process parameters and data augmentation and do the following: (i) This study developed a more detailed phase classification strategy for HAEs to enable the model to predict the complex multiphase of HAEs; (ii) The process parameters of AM affect the formation of the HEAs phase. In this study, the process parameters of AM are added to the model features to improve the model's accuracy in predicting the HEAs phase; (iii) To alleviate the data scarcity dilemma of HEAs, we use data augmentation to improve DL's performance further. The results show that after adding AM process parameters to the model and performing data augmentation, the DL model's accuracy to 91.23 %. In addition, we manufactured four new HEAs through AM experiments to verify the robustness and practicality of their algorithms.