Temperature evolution prediction for laser directed energy deposition enabled by finite element modelling and bi-directional gated recurrent unit

doi:10.1007/s40436-024-00511-2

Advances in Manufacturing ›› 2025, Vol. 13 ›› Issue (3): 668-687.doi: 10.1007/s40436-024-00511-2

• • 上一篇

Temperature evolution prediction for laser directed energy deposition enabled by finite element modelling and bi-directional gated recurrent unit

Kai-Xiong Hu^1,3, Kai Guo¹, Wei-Dong Li², Yang-Hui Wang¹

1. School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, 430063, Hubei, People's Republic of China;
2. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China;
3. Hubei Longzhong Laboratory, Xiangyang, 441000, Hubei, People's Republic of China

收稿日期:2023-09-22 修回日期:2023-11-30 发布日期:2025-09-19
通讯作者: Wei-Dong Li,E-mail:weidongli@usst.edu.cn E-mail:weidongli@usst.edu.cn
作者简介:Kai-Xiong Hu received the Ph.D. degree in Materials Engineering from Hamburg University of Technology in Germany in 2017. Now he is an Associate Professor at the School of Transportation and Logistics Engineering, Wuhan University of Technology. His research interests include quality control in additive manufacturing.
Kai Guo received his M.S. degree in Mechanical Engineering from Wuhan University of Technology in 2023. His research interests focus on finite element simulation of additive manufacturing.
Wei-Dong Li is Chang Jiang Chair Professor of University of Shanghai for Science and Technology (China). He has been a Chair at School of Mechanical and Automotive Engineering, Coventry University (U.K.) since 2013. He is a Fellow of Institution of Engineering and Technology (FIET), and a Fellow of Institution of Mechanical Engineers (FIMechE). His research interests include sustainable manufacturing and human–robot collaboration. In the areas, he has published 260 research papers in international journals and conferences, and 5 books (Springer).
Yang-Hui Wang received his M.S. degree in Mechanical Engineering from Wuhan University of Technology in 2023. He is currently pursuing his Ph.D. degree at Wuhan University of Technology. His research area is additive manufacturing.
基金资助:
This research was supported by the National Natural Science Foundation of China (Grant No. 51975444), the International Cooperative Project of the Ministry of Science and Technology of China (Grant No. G2022013009), and the Science and Technology Commission of Shanghai Municipality (Grant No. 23010503700).

Temperature evolution prediction for laser directed energy deposition enabled by finite element modelling and bi-directional gated recurrent unit

Kai-Xiong Hu^1,3, Kai Guo¹, Wei-Dong Li², Yang-Hui Wang¹

1. School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, 430063, Hubei, People's Republic of China;
2. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China;
3. Hubei Longzhong Laboratory, Xiangyang, 441000, Hubei, People's Republic of China

Received:2023-09-22 Revised:2023-11-30 Published:2025-09-19
Supported by:
This research was supported by the National Natural Science Foundation of China (Grant No. 51975444), the International Cooperative Project of the Ministry of Science and Technology of China (Grant No. G2022013009), and the Science and Technology Commission of Shanghai Municipality (Grant No. 23010503700).

摘要/Abstract

摘要： In the laser-directed energy deposition (L-DED) process, achieving an efficient temperature evolution prediction of molten pools is critical but challenging. To resolve this issue, this study presents an innovative approach that integrates a high-fidelity finite element (FE) model and an effective machine-learning model. Firstly, a high-fidelity FE model for the L-DED process was developed and subsequently validated through an experimental examination of the cross-sectional geometries of the molten pools and temperature fields of the substrate. Then, a Bi-directional gated recurrent unit (Bi-GRU) was formulated to predict the temperature evolution of the molten pools during L-DED. By training the Bi-GRU model using datasets generated from the FE model, it was deployed to efficiently predict the temperature evolution of the manufactured multi-layer single-bead walls. The results demonstrated that, in terms of the average mean absolute error, this approach outperformed other approaches designed based on the gated recurrent unit (GRU) model, long short-term memory model, and recurrent neural network models by 26.7%, 52.1%, and 65.2%, respectively. The results also showed that the prediction time required by this approach, once trained, was significantly reduced by five orders of magnitude compared with the FE model. Therefore, this approach accurately predicts the temperature evolution of multi-layer single-bead walls in a computationally efficient manner. This approach is a promising solution for supporting the real-time control of the L-DED process in industrial applications.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00511-2

关键词: Laser-directed energy deposition (L-DED), Temperature evolution, Finite element (FE) modelling, Bi-directional gated recurrent unit (Bi-GRU), Additive manufacturing (AM)

Abstract: In the laser-directed energy deposition (L-DED) process, achieving an efficient temperature evolution prediction of molten pools is critical but challenging. To resolve this issue, this study presents an innovative approach that integrates a high-fidelity finite element (FE) model and an effective machine-learning model. Firstly, a high-fidelity FE model for the L-DED process was developed and subsequently validated through an experimental examination of the cross-sectional geometries of the molten pools and temperature fields of the substrate. Then, a Bi-directional gated recurrent unit (Bi-GRU) was formulated to predict the temperature evolution of the molten pools during L-DED. By training the Bi-GRU model using datasets generated from the FE model, it was deployed to efficiently predict the temperature evolution of the manufactured multi-layer single-bead walls. The results demonstrated that, in terms of the average mean absolute error, this approach outperformed other approaches designed based on the gated recurrent unit (GRU) model, long short-term memory model, and recurrent neural network models by 26.7%, 52.1%, and 65.2%, respectively. The results also showed that the prediction time required by this approach, once trained, was significantly reduced by five orders of magnitude compared with the FE model. Therefore, this approach accurately predicts the temperature evolution of multi-layer single-bead walls in a computationally efficient manner. This approach is a promising solution for supporting the real-time control of the L-DED process in industrial applications.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00511-2

Key words: Laser-directed energy deposition (L-DED), Temperature evolution, Finite element (FE) modelling, Bi-directional gated recurrent unit (Bi-GRU), Additive manufacturing (AM)

Kai-Xiong Hu, Kai Guo, Wei-Dong Li, Yang-Hui Wang. Temperature evolution prediction for laser directed energy deposition enabled by finite element modelling and bi-directional gated recurrent unit[J]. Advances in Manufacturing, 2025, 13(3): 668-687.

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Temperature evolution prediction for laser directed energy deposition enabled by finite element modelling and bi-directional gated recurrent unit

Temperature evolution prediction for laser directed energy deposition enabled by finite element modelling and bi-directional gated recurrent unit

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