Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators

• URL: http://arxiv.org/abs/2307.01804v1
• Date: Tue, 4 Jul 2023 16:17:59 GMT
• Title: Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators
• Authors: Jiangce Chen, Wenzhuo Xu, Martha Baldwin, Bj\"orn Nijhuis, Ton van den Boogaard, Noelia Grande Guti\'errez, Sneha Prabha Narra, Christopher McComb
• Abstract summary: This paper presents a data-driven model that captures the local temperature evolution during the additive manufacturing process. It is tested on numerical simulations based on the Discontinuous Galerkin Finite Element Method for the Direct Energy Deposition process. The results demonstrate that the model achieves high fidelity as measured by $R2$ and maintains generalizability to geometries that were not included in the training process.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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