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3D temperature field prediction in direct energy deposition of metals using physics informed neural network

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Abstract

Predicting the temperature field during the direct energy deposition (DED) process is vital for the microstructure control and property tuning of fabricated metals. The widely used data-driven machine learning method for accurate temperature prediction, however, is impractical and computation-intensive due to its sole reliance on large datasets; also being a black-box model in nature, it lacks interpretability. We propose a physics informed neural network (PINN) model, which adopts a novel physics-data hybrid method by embedding the heat transfer law into the loss function of the neural network, to model the temperature field in both single-layer and multi-layer DED. The results show that the PINN-based models with additional extrapolation ability can accurately predict temperatures with a mean relative error of 4.83%, and achieve identical prediction accuracy with only 20% of the labeled data required for training the data-driven deep neural network. The proposed model is more explainable in terms of the physics of the DED process and is also applicable for the DED of different metals.

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Availability of data and material

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

All the codes of this study are available from the corresponding author upon reasonable request.

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Funding

This work was supported by the National Key R&D Program of China [2018YFC0310400], and Guangzhou Risong Intelligent Technology Holding Co., Ltd. China, [Grant numbers: 2020-L021].

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Authors and Affiliations

  1. Shanghai Key Laboratory of Materials Laser Processing and Modification, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Jibing Xie, Ze Chai, Luming Xu, Xukai Ren, Sheng Liu & Xiaoqi Chen

  2. College of Engineering, Swinburne University of Technology, Hawthorn, VIC, 3122, Australia

    Xiaoqi Chen

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  1. Jibing Xie
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  2. Ze Chai
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  3. Luming Xu
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  4. Xukai Ren
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  5. Sheng Liu
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Contributions

Jibing Xie: investigation, methodology, coding, analyzing, writing—original draft. Ze Chai: writing—review and editing. Xukai Ren: writing—review and editing. Luming Xu: experimentation. Sheng Liu: data analysis. Xiaoqi Chen: conceptualization, funding acquisition, writing—review, and revision.

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Correspondence to Xiaoqi Chen.

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Xie, J., Chai, Z., Xu, L. et al. 3D temperature field prediction in direct energy deposition of metals using physics informed neural network. Int J Adv Manuf Technol 119, 3449–3468 (2022). https://doi.org/10.1007/s00170-021-08542-w

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