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The International Journal of Advanced Manufacturing Technology

Erschienen in:

07.01.2022 | ORIGINAL ARTICLE

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

verfasst von: Jibing Xie, Ze Chai, Luming Xu, Xukai Ren, Sheng Liu, Xiaoqi Chen

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 5-6/2022

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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.

Vorheriger Artikel Millisecond fibre laser drilling of thick-section aerospace alloy

Nächster Artikel Experimental study of vibration signal for a prognostic system to prevent tool breakage in micro gun drilling

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Titel: 3D temperature field prediction in direct energy deposition of metals using physics informed neural network
verfasst von: Jibing Xie
Ze Chai
Luming Xu
Xukai Ren
Sheng Liu
Xiaoqi Chen
Publikationsdatum: 07.01.2022
Verlag: Springer London
Erschienen in: The International Journal of Advanced Manufacturing Technology / Ausgabe 5-6/2022
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-021-08542-w

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