Microstructure Knowledge Systems for Capturing Process-Structure Evolution Linkages

Published

June 14, 2017

Author(s)

James A. Warren, Daniel Wheeler, David Brough, Surya Kalidindi

Abstract

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.

Citation

ACTA Materialia

Pub Type

Journals

Citation

Warren, J. , Wheeler, D. , Brough, D. and Kalidindi, S. (2017), Microstructure Knowledge Systems for Capturing Process-Structure Evolution Linkages, ACTA Materialia (Accessed November 23, 2024)