Effective design space exploration of gradient nanostructured materials using active learning based surrogate models

Xin Chen, Haofei Zhou, Yumeng Li

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article number108085
JournalMaterials and Design
Volume183
DOIs
StatePublished - Dec 5 2019

Keywords

  • Artificial intelligence
  • Gaussian processes
  • Gradient nanostructured metals
  • Materials design
  • Surrogate modeling

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering

Fingerprint Dive into the research topics of 'Effective design space exploration of gradient nanostructured materials using active learning based surrogate models'. Together they form a unique fingerprint.

  • Cite this