TY - JOUR
T1 - Effective design space exploration of gradient nanostructured materials using active learning based surrogate models
AU - Chen, Xin
AU - Zhou, Haofei
AU - Li, Yumeng
N1 - Funding Information:
H. Zhou acknowledges support by Zhejiang University through “Hundred Talents Program”. X. Chen and Y. Li are grateful for the support provided by the University of Illinois at Urbana-Champaign . The authors are grateful to Prof. H.J. Gao for insightful discussions.
Funding Information:
H. Zhou acknowledges support by Zhejiang University through ?Hundred Talents Program?. X. Chen and Y. Li are grateful for the support provided by the University of Illinois at Urbana-Champaign. The authors are grateful to Prof. H.J. Gao for insightful discussions.
Publisher Copyright:
© 2019
PY - 2019/12/5
Y1 - 2019/12/5
N2 - Inspired by gradient structures in the nature, Gradient Nanostructured (GNS) metals have emerged as a new class of materials with tunable microstructures. GNS metals can exhibit unique combinations of material properties in terms of ultrahigh strength, good tensile ductility and enhanced strain hardening, superior fatigue and wear resistance. However, it is still challenging to fully understand the fundamental gradient structure-property relationship, which hinders the rational design of GNS metals with optimized target properties. In this paper, we developed an adaptive design framework based on surrogate modeling to investigate how the grain size gradient and twin thickness gradient affect the strength of GNS metals. The Gaussian Process (GP) based surrogate modeling technique with adaptive sequential sampling is employed to develop the surrogate models for the gradient structure-property relationship. The proposed adaptive design integrates physics-based simulation, surrogate modeling, uncertainty quantification and optimization, which can efficiently explore the design space and identify the optimized design of GNS metals with maximum strength using limited sampling data generated from high fidelity but computational expensive physics-based simulations.
AB - Inspired by gradient structures in the nature, Gradient Nanostructured (GNS) metals have emerged as a new class of materials with tunable microstructures. GNS metals can exhibit unique combinations of material properties in terms of ultrahigh strength, good tensile ductility and enhanced strain hardening, superior fatigue and wear resistance. However, it is still challenging to fully understand the fundamental gradient structure-property relationship, which hinders the rational design of GNS metals with optimized target properties. In this paper, we developed an adaptive design framework based on surrogate modeling to investigate how the grain size gradient and twin thickness gradient affect the strength of GNS metals. The Gaussian Process (GP) based surrogate modeling technique with adaptive sequential sampling is employed to develop the surrogate models for the gradient structure-property relationship. The proposed adaptive design integrates physics-based simulation, surrogate modeling, uncertainty quantification and optimization, which can efficiently explore the design space and identify the optimized design of GNS metals with maximum strength using limited sampling data generated from high fidelity but computational expensive physics-based simulations.
KW - Artificial intelligence
KW - Gaussian processes
KW - Gradient nanostructured metals
KW - Materials design
KW - Surrogate modeling
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U2 - 10.1016/j.matdes.2019.108085
DO - 10.1016/j.matdes.2019.108085
M3 - Article
AN - SCOPUS:85070857935
SN - 0261-3069
VL - 183
JO - International Journal of Materials in Engineering Applications
JF - International Journal of Materials in Engineering Applications
M1 - 108085
ER -