Image-Based Quantitative Mapping of Structure Property Relationships in Polymer Composite Materials

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Year : 2026 | Volume : 14 | 03 | Page :
    By

    Mohandass G,

  • B Ram Priya,

  • R Arangasamy,

  • C Sridhathan,

  1. Professor, Department of Biomedical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
  2. Professor, Department of Electronics and Electrical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
  3. Professor, Department of Biomedical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
  4. Professor, Department of Electronics and Communication Engineering, KCG College of Technology, Karapakkam, Chennai, Tamil Nadu, India

Abstract

The performance of polymer composite materials is intrinsically governed by their microstructural architecture, which is shaped by manufacturing conditions and constituent interactions. Despite extensive experimental characterization efforts, establishing transparent and quantitative structure–property relationships from microstructural images remains a challenge. In this study, an explainable image-driven framework is developed to systematically correlate microstructural features with composite property indicators. Microstructure images are processed to identify voids, fibers, and filler phases, from which physically meaningful descriptors related to morphology, dispersion, and connectivity are extracted. These descriptors are integrated into an interpretable regression model that enables accurate prediction of composite behavior while revealing the relative influence of individual microstructural features. The results demonstrate that void fraction, fiber orientation dispersion, and filler connectivity density are the dominant contributors governing composite performance. By combining image analysis with explainable learning, the proposed framework advances data-driven composite characterization by enabling physically interpretable insights and supporting microstructure-informed materials design.

Keywords: Polymer composites; Microstructure image analysis; Structure–property relationships; Explainable machine learning; Materials informatics

How to cite this article:
Mohandass G, B Ram Priya, R Arangasamy, C Sridhathan. Image-Based Quantitative Mapping of Structure Property Relationships in Polymer Composite Materials. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
Mohandass G, B Ram Priya, R Arangasamy, C Sridhathan. Image-Based Quantitative Mapping of Structure Property Relationships in Polymer Composite Materials. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=243060


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Ahead of Print Subscription Original Research
Volume 14
03
Received 28/02/2026
Accepted 07/04/2026
Published 06/05/2026
Publication Time 67 Days
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