Microstructure-based knowledge systems for capturing process-structure evolution linkages

•Reviews and advances a data science framework call Materials Knowledge Systems.•Two different treatments are presented for functions of the local state variable.•New method presented to learn the underlying embedded physics from numerical models. This paper reviews and advances a data science frame...

Full description

Saved in:
Bibliographic Details
Published in:Current opinion in solid state & materials science 2017-06, Vol.21 (3), p.129-140
Main Authors: Brough, David B., Wheeler, Daniel, Warren, James A., Kalidindi, Surya R.
Format: Article
Language:English
Subjects:
Cahn-Hilliard model
Homogenization
Localization
Materials Knowledge Systems
Multiscale modeling
Phase field
Spectral representations
Structure evolution
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Reviews and advances a data science framework call Materials Knowledge Systems.•Two different treatments are presented for functions of the local state variable.•New method presented to learn the underlying embedded physics from numerical models. This paper reviews and advances a data science framework for capturing and communicating critical information regarding the evolution of material structure in spatiotemporal multiscale simulations. This approach is called the MKS (Materials Knowledge Systems) framework, and was previously applied successfully for capturing mainly the microstructure-property linkages in spatial multiscale simulations. This paper generalizes this framework by allowing the introduction of different basis functions, and explores their potential benefits in establishing the desired process-structure-property (PSP) linkages. These new developments are demonstrated using a Cahn-Hilliard simulation as an example case study, where structure evolution was 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 through Green’s function based influence kernels rather than differential equations, and potentially offers significant computational advantages in problems where numerical integration schemes are challenging to optimize. With this extension, we have now established a comprehensive framework for capturing PSP linkages for multiscale materials modeling and simulations in both space and time.
ISSN:1359-0286
DOI:10.1016/j.cossms.2016.05.002