Residual thermal stress prediction for continuous tool-paths in wire-arc additive manufacturing: a three-level data-driven method

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.

KEYWORDS:

• Wire-arc additive manufacturing
• thermal field history prediction
• residual thermal stress field prediction
• machine learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 51975518], the Science Fund for Creative Research Groups of the National Natural Science Foundation of China [grant number 51821093], and Ningbo Science and Technology Plan Project [grant number 2019B10072].

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.