TY - JOUR
T1 - Hierarchical n-point polytope functions for quantitative representation of complex heterogeneous materials and microstructural evolution
AU - Chen, Pei En
AU - Xu, Wenxiang
AU - Chawla, Nikhilesh
AU - Ren, Yi
AU - Jiao, Yang
N1 - Funding Information:
This work is supported by ACS Petroleum Research Fund under Grant No. 56474-DNI10 (Program manager: Dr. Burtrand Lee). Y.J. and N.C. acknowledge the Office of Knowledge Enterprise Development (OKED) and the Fulton Schools of Engineering for a seed grant.
PY - 2019/10/15
Y1 - 2019/10/15
N2 - Effective and accurate characterization and quantification of complex microstructure of a heterogeneous material and its evolution under external stimuli are very challenging, yet crucial to achieving reliable material performance prediction, processing optimization and advanced material design. Here, we address this challenge by developing a set of hierarchical statistical microstructural descriptors, which we call the “n-point polytope functions” Pn, for quantitative characterization, representation and modeling of complex material microstructure and its evolution. These polytope functions successively include higher-order n-point statistics of the features of interest in the microstructure in a concise, expressive, explainable, and universal manner; and can be directly computed from multi-modal imaging data. We develop highly efficient computational tools to directly extract the Pn functions up to n = 8 from multi-modal imaging data. Using simple model microstructures, we show that these statistical descriptors effectively “decompose” the structural features of interest into a set of “polytope basis”, allowing one to easily detect any underlying symmetry or emerging features during the structural evolution. We apply the Pn functions to quantify and model a variety of heterogeneous material systems, including particle-reinforced composites, metal-ceramic composites, concretes, porous materials; as well as the microstructural evolution in an aged lead-tin alloy. Our results indicate that the Pn functions can offer a practical set of basis for quantitative microstructure representation (QMR), for both static 3D complex microstructure and 4D microstructural evolution of a wide spectrum of heterogeneous material systems.
AB - Effective and accurate characterization and quantification of complex microstructure of a heterogeneous material and its evolution under external stimuli are very challenging, yet crucial to achieving reliable material performance prediction, processing optimization and advanced material design. Here, we address this challenge by developing a set of hierarchical statistical microstructural descriptors, which we call the “n-point polytope functions” Pn, for quantitative characterization, representation and modeling of complex material microstructure and its evolution. These polytope functions successively include higher-order n-point statistics of the features of interest in the microstructure in a concise, expressive, explainable, and universal manner; and can be directly computed from multi-modal imaging data. We develop highly efficient computational tools to directly extract the Pn functions up to n = 8 from multi-modal imaging data. Using simple model microstructures, we show that these statistical descriptors effectively “decompose” the structural features of interest into a set of “polytope basis”, allowing one to easily detect any underlying symmetry or emerging features during the structural evolution. We apply the Pn functions to quantify and model a variety of heterogeneous material systems, including particle-reinforced composites, metal-ceramic composites, concretes, porous materials; as well as the microstructural evolution in an aged lead-tin alloy. Our results indicate that the Pn functions can offer a practical set of basis for quantitative microstructure representation (QMR), for both static 3D complex microstructure and 4D microstructural evolution of a wide spectrum of heterogeneous material systems.
KW - Heterogeneous materials
KW - Microstructure evolution
KW - Multi-modal imaging data
KW - N-point polytope functions
KW - Quantitative microstructure representation (QMR)
UR - http://www.scopus.com/inward/record.url?scp=85071608514&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071608514&partnerID=8YFLogxK
U2 - 10.1016/j.actamat.2019.08.045
DO - 10.1016/j.actamat.2019.08.045
M3 - Article
AN - SCOPUS:85071608514
SN - 1359-6454
VL - 179
SP - 317
EP - 327
JO - Acta Materialia
JF - Acta Materialia
ER -