A physics-informed neural network framework to predict 3D temperature field without labeled data in process of laser metal deposition.

To predict thermal behaviors during the laser metal deposition process, traditional approaches like experiments or finite-element methods(FEM) can be quite time-consuming, while data-driven machine learning models rely on large labeled datasets, which are too expensive to obtain. To fully exploit the potential of machine learning and release it from the dataset dependence, a physics-informed neural network framework that does not require any labeled data to predict 3D temperature field was proposed. The model used customized loss functions by replacing the original data loss with physical losses of heat conduction, convection and radiation.The implementation of nonlinear temperature-dependent material properties and the scaling of model inputs and outputs were involved. By iterative training, the model achieved accurate predictions of approximately 2% maximum relative error compared with FEM results. The transfer learning part was utilized for scenarios of different manufacturing parameters, and took about 1/3 of the calculation time as FEM did without losing accuracy. All the results above validated the high effectiveness and accuracy of the proposed framework.