Capturing Local Temperature Evolution During Additive Manufacturing Through Fourier Neural Operator

Jiangce Chen, Wenzhuo Xu, Martha Baldwin, Björn Nijhuis, Ton van den Boogaard, Noelia Grande Gutiérrez, Sneha Prabha Narra, Christopher McComb*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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 R2 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 R2 and maintains generalizability to geometries that were not included in the training process.

Original languageEnglish
Title of host publication43rd Computers and Information in Engineering Conference (CIE)
PublisherAmerican Society of Mechanical Engineers
Number of pages15
Volume2
ISBN (Electronic)978-0-7918-8729-5
DOIs
Publication statusPublished - 21 Nov 2023
EventASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023 - Boston, United States
Duration: 20 Aug 202323 Aug 2023

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume2

Conference

ConferenceASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023
Abbreviated titleIDETC-CIE
Country/TerritoryUnited States
CityBoston
Period20/08/2323/08/23

Keywords

  • NLA

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