Neural Network Accelerated Process Design of Polycrystalline Microstructures
This work addresses the computational bottleneck in material science for designing microstructures, offering a significant speed-up for researchers and engineers, though it is incremental as it applies an existing neural network approach to a specific domain problem.
The paper tackled the computational challenge of optimizing material processing paths for polycrystalline microstructures by developing a neural network-based method with physics-infused constraints, which was 686 times faster and achieved 0.053% error in homogenized stiffness compared to traditional simulators.
Computational experiments are exploited in finding a well-designed processing path to optimize material structures for desired properties. This requires understanding the interplay between the processing-(micro)structure-property linkages using a multi-scale approach that connects the macro-scale (process parameters) to meso (homogenized properties) and micro (crystallographic texture) scales. Due to the nature of the problem's multi-scale modeling setup, possible processing path choices could grow exponentially as the decision tree becomes deeper, and the traditional simulators' speed reaches a critical computational threshold. To lessen the computational burden for predicting microstructural evolution under given loading conditions, we develop a neural network (NN)-based method with physics-infused constraints. The NN aims to learn the evolution of microstructures under each elementary process. Our method is effective and robust in finding optimal processing paths. In this study, our NN-based method is applied to maximize the homogenized stiffness of a Copper microstructure, and it is found to be 686 times faster while achieving 0.053% error in the resulting homogenized stiffness compared to the traditional finite element simulator on a 10-process experiment.