2024/11/27 by M. Manav, Manav, Manav, Nathanaël Perraudin +11
Engineering · #Additive Manufacturing Materials and Processes #Additive Manufacturing and 3D Printing Technologies #Applied Physics (physics.app-ph) #Computational Physics (physics.comp-ph) #FOS: Physical sciences
paper · pdf · doi:10.48550/arxiv.2411.18048
openalex publication_date 2024/11/27 · openalex created_date 2024/12/05 · openalex updated_date 2026/07/28
We present a data-driven, differentiable neural network model designed to learn the temperature field, its gradient, and the cooling rate, while implicitly representing the melt pool boundary as a level set in laser powder bed fusion. The physics-guided model combines fully connected feed-forward neural networks with Fourier feature encoding of the spatial coordinates and laser position. Notably, our differentiable model allows for the computation of temperature derivatives with respect to position, time, and process parameters using autodifferentiation. Moreover, the implicit neural representation of the melt pool boundary as a level set enables the inference of the solidification rate and the rate of change in melt pool geometry relative to process parameters. The model is trained to learn the top view of the temperature field and its spatiotemporal derivatives during a single-track laser powder bed fusion process, as a function of three process parameters, using data from high-fidelity thermo-fluid simulations. The model accuracy is evaluated and compared to a state-of-the-art convolutional neural network model, demonstrating strong generalization ability and close agreement with high-fidelity data.