Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators

TL;DR

This paper introduces a Fourier Neural Operator-based model for accurately capturing local temperature evolution in additive manufacturing, emphasizing the importance of relative performance metrics like R^2 over traditional MSE.

Contribution

The paper presents a novel data-driven approach using Fourier Neural Operators for local temperature prediction in AM, with an improved evaluation metric for fidelity.

Findings

Model achieves high R^2 scores indicating accurate local temperature predictions.

The approach generalizes well to unseen geometries in simulations.

Using R^2 provides a more meaningful fidelity measure than MSE in this context.

Abstract

High-fidelity, data-driven models that can quickly simulate thermal behavior during additive manufacturing (AM) are crucial for improving the performance of AM technologies in multiple areas, such as part design, process planning, monitoring, and control. However, the complexities of part geometries make it challenging for current models to maintain high accuracy across a wide range of geometries. Additionally, many models report a low mean square error (MSE) across the entire domain (part). However, in each time step, most areas of the domain do not experience significant changes in temperature, except for the heat-affected zones near recent depositions. Therefore, the MSE-based fidelity measurement of the models may be overestimated. This paper presents a data-driven model that uses Fourier Neural Operator to capture the local temperature evolution during the additive manufacturing process. In addition, the authors propose to evaluate the model using the $R^2$ metric, which provides a relative measure of the model's performance compared to using mean temperature as a prediction. The model was tested on numerical simulations based on the Discontinuous Galerkin Finite Element Method for the Direct Energy Deposition process, and the results demonstrate that the model achieves high fidelity as measured by $R^2$ and maintains generalizability to geometries that were not included in the training process.