Configuration feature extraction and mechanical properties prediction of particle reinforced metal matrix composites

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

6 Scopus Citations
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Author(s)

  • Zichang Lin
  • Yishi Su
  • Jingyu Yang
  • Xushun Chai
  • Xuyang Liu
  • Qiubao Ouyang
  • Di Zhang

Detail(s)

Original languageEnglish
Article number101688
Journal / PublicationComposites Communications
Volume42
Online published5 Aug 2023
Publication statusPublished - Oct 2023

Abstract

Improving the strength-toughness matching properties has become a key issue in metal matrix composites, which can be achieved by some specific designs of composite configurations (i.e. spatial distribution of reinforced particles). To better interpret the composite configuration effect on mechanical properties, it is necessary to develop comprehensive feature descriptors for composite configurations. In this work, topological data analysis and particle clustering parameter are applied to extract feature descriptors for composite configurations of SiCp/Al composites, which are effective to distinguish different types of composite configurations. In addition, based on the dataset constructed by finite element analysis, machine learning algorithms are applied to predict the composite configurations-mechanical properties of SiCp/Al composites. Then, particle swarm optimization is used to search the composite configurations with extreme mechanical properties predicted by machine learning model. The results of this work indicate that the composite configurations with larger particle clustering parameter and persistent entropy will yield better strength-toughness matching. © 2023 Elsevier Ltd

Research Area(s)

  • Machine learning, Metal matrix composites, Strength-toughness matching, Topological data analysis

Citation Format(s)

Configuration feature extraction and mechanical properties prediction of particle reinforced metal matrix composites. / Lin, Zichang; Su, Yishi; Yang, Jingyu et al.
In: Composites Communications, Vol. 42, 101688, 10.2023.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review