MeltpoolINR: Predicting temperature field, melt pool geometry, and their rate of change in laser powder bed fusion

TL;DR

This paper introduces a physics-guided neural network model that predicts temperature distribution, melt pool geometry, and their rates of change in laser powder bed fusion, enabling detailed process analysis and control.

Contribution

The novel model combines neural networks with Fourier features and implicit level set representations to accurately predict and analyze melt pool dynamics from simulation data.

Findings

Model achieves high accuracy in temperature and melt pool predictions.

Demonstrates strong generalization to unseen process parameters.

Enables computation of derivatives for process optimization.

Abstract

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.