Accelerating phase-field simulation of three-dimensional microstructure evolution in laser powder bed fusion with composable machine learning predictions

Abstract

Phase-field (PF) modeling is a versatile physics-based computational method that has been used to simulate the evolution of microstructures. The PF method can produce accurate microstructures but suffers from a high computational cost, limiting its use in length scales relevant to additive manufacturing. Using small-scale PF simulations as training data, we trained a surrogate machine learning (ML) model as a computationally cheaper alternative. We use a three-dimensional (3D) U-Net convolutional neural network and learn microstructure evolution in a supervised fashion. With initial microstructure and thermal history as inputs, the ML model can predict the resulting grain orientations at a high accuracy compared to the PF model. Computationally, the ML model is orders of magnitudes faster than the direct PF simulation in a GPU implementation, and scales favorably with increasing number of cells. By spatio-temporally composing multiple ML model predictions at the small-scale, we demonstrate a large-scale 64-layer simulation of a 2 mm × 2 mm × 2 mm cube for a powder bed fusion additive manufacturing process. The ML results revealed a mixture of equiaxed, columnar, and curved grains, comparable to experimental observations. Microstructures resulting from different toolpath strategies, and lack of fusion defects are also demonstrated. This approach paves the way for microstructure-driven process design.