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A Review of Application of Machine Learning in Design, Synthesis, and Characterization of Metal Matrix Composites: Current Status and Emerging Applications

  • Machine Learning in Design, Synthesis, and Characterization of Composite Materials
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Abstract

In this article we provide an overview on the current and emerging applications of machine learning (ML) in the design, synthesis, and characterization of metal matrix composites (MMC). We have demonstrated that ML methods can be applied in three distinct categories, namely property prediction, microstructure analysis, and process optimization, which are associated with three major classes of ML techniques, i.e., regression, classification, and optimal control, respectively. ML algorithms have been successfully applied for prediction of mechanical, tribological, corrosion, and wetting properties of different MMCs. However, ML methods (e.g., computer vision, which is suitable for microstructural characterization and defect detections) and optimization algorithms (e.g., reinforcement learning) have not been fully utilized for design, processing, and characterization of metal matrix composites despite their enormous capacities. We conclude that ML methods are promising not only to predict various properties but also to automate microstructural analysis and optimization of manufacturing MMCs.

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Authors and Affiliations

  1. Department of Industrial and Manufacturing Engineering, University of Wisconsin Milwaukee, Milwaukee, WI, 53211, USA

    Amir Kordijazi

  2. Department of Material Science and Engineering, University of Wisconsin Milwaukee, Milwaukee, WI, 53211, USA

    Amir Kordijazi & Pradeep Rohatgi

  3. Department of Electrical Engineering and Computer Science, University of Wisconsin Milwaukee, Milwaukee, WI, 53211, USA

    Tian Zhao, Jun Zhang & Khaled Alrfou

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  1. Amir Kordijazi
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Correspondence to Amir Kordijazi.

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Kordijazi, A., Zhao, T., Zhang, J. et al. A Review of Application of Machine Learning in Design, Synthesis, and Characterization of Metal Matrix Composites: Current Status and Emerging Applications. JOM 73, 2060–2074 (2021). https://doi.org/10.1007/s11837-021-04701-2

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