Surrogate modeling of microstructure prediction in additive manufacturing

Published

November 21, 2024

Author(s)

Paul Witherell, Sankaran Mahadevan, Paromita Nath, Arulmurugan Senthilnathan

Abstract

Variability in the additive manufacturing process and powder material properties affect the microstructure which influences the macro-scale material properties. Systematic quantification and propagation of this uncertainty require numerous process-structure-property (P-S-P) simulations. However, the high computational cost of the P-S simulation (thermal model), which relates the microstructure to the process parameters, necessitates the need for inexpensive surrogate models. Moreover, the PS simulation generates a high-dimensional microstructure image; this presents a challenge in constructing a surrogate model whose inputs are process parameters and output is the microstructure image. This work addresses this challenge and develops a novel approach to surrogate modeling. First, a dimension reduction method based on combining the concepts of image moment invariants and principal components is used to map the high-dimensional microstructure image into latent space. A surrogate model is then constructed in the low-dimensional latent space to predict the principal features, which are then mapped to the original dimension to obtain the microstructure image. The surrogate model-predicted microstructure image is verified against the original physics model prediction (thermal model + phase-field) of the microstructure image, using Hu moments. Developing this surrogate modeling approach paves the way for solving computationally expensive tasks such as uncertainty quantification and process parameter optimization.

Citation

Computational Materials Science

Volume

247

Pub Type

Journals

Download Paper

https://doi.org/10.1016/j.commatsci.2024.113536

Local Download

Keywords

Additive Manufacturing, Microstructure, Surrogate Modelling

Citation

Witherell, P. , Mahadevan, S. , Nath, P. and Senthilnathan, A. (2024), Surrogate modeling of microstructure prediction in additive manufacturing, Computational Materials Science, [online], https://doi.org/10.1016/j.commatsci.2024.113536, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=959176 (Accessed March 31, 2025)