ABSTRACT
Continuous tool-path is often chose to improve the deposition efficiency and surface accuracy of metal additive manufacturing, while it also causes large residual thermal stress, which will result in part deformation and performance degradation. This paper focused on wire-arc additive manufacturing (WAAM) with arbitrary part geometries and continuous tool-paths, and proposed a three-level data-driven method to predict the residual thermal stress filed accurately and rapidly. The first two-level of the proposed method predict the thermal field history of the whole WAAM process. The third level of the proposed method realises the residual thermal stress field prediction of WAAM based on above prediction results. Each level is based on a machine learning method, and their data were obtained based on the finite element method. The prediction accuracy of the proposed method exceeded 92%, and the time cost of one prediction process was only at the second level.
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No potential conflict of interest was reported by the author(s).
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Notes on contributors
Zeyu Zhou
Zeyu Zhou is currently pursuing the M.S. degree in mechanical manufacturing and automation at Zhejiang University, Hangzhou, China. His research interest includes additive manufacturing and artificial intelligence.
Hongyao Shen
Hongyao Shen is working as an associate professor in Zhejiang University, Hangzhou, China. His research interest includes additive manufacturing and high performance CNC machining.
Bing Liu
Bing Liu is currently pursuing the Ph.D. degree in mechanical manufacturing and automation at Zhejiang University, Hangzhou, China. His research interest includes additive manufacturing and hybrid manufacturing.
Wangzhe Du
Wangzhe Du is currently pursuing the Ph.D. degree in mechanical manufacturing and automation at Zhejiang University, Hangzhou, China. His research interest includes defect detection, deep learning, and computer vision.
Jiaao Jin
Jiaao Jin is currently pursuing the M.S. degree in mechanical manufacturing and automation at Zhejiang University, Hangzhou, China. His research interest includes additive manufacturing and point cloud processing.
Jiahao Lin
Jiahao Lin is currently pursuing the M.S. degree in mechanical manufacturing and automation at Zhejiang University, Hangzhou, China. His research interest includes additive manufacturing and heat treatment.