Surrogate modeling of microstructure prediction in additive manufacturing

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 P-S 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.