Microstructure Knowledge Systems for Capturing Process-Structure Evolution Linkages
James A. Warren, Daniel Wheeler, David Brough, Surya Kalidindi
This paper develops and demonstrates a novel data science framework for capturing and communicating critical information regarding the evolution of material structure in spatiotemporal multiscale simulations of various processing operations employed by the manufacturing industry. This new framework is essentially an extension of the MKS (Materials Knowledge Systems) framework that was previously demonstrated for capturing microstructure- property linkages in spatial multiscale simulations. Furthermore, this paper presents a derivation of the MKS framework using different basis functions, and explores their potential benefits in establishing the desired process- structure-property (PSP) linkages. These developments are demonstrated using a Cahn-Hilliard simulation, where structure evolutions were predicted three orders of magnitude faster than an optimized numerical integration algorithm. This study suggests that the MKS localization framework provides an alternate method to learn the underlying embedded physics in a numerical model expressed as Green's function based influence kernels rather than differential equations, and potentially has computational advantages in problems where numerical integration schemes are challenging are challenging to optimize. With this extension, we have now established a comprehensive framework for capturing PSP linkages for spatiotemporal multiscale materials modeling and simulations.