A Comprehensive Survey of Polymer Detection Techniques and Computer-Based Analysis Methods for Advanced Material Characterization

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

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

    M. Vasavi,

  • T. Prem Chander,

  • Murugan Ambigapathy,

  1. Sr.Assistant Professor, Department of Computer Science and Engineering (Cyber Security), CVR College of Engineering, Hyderabad, Telangana, India
  2. Associate Professor, Department of Computer Science and Engineering, Matrusri Engineering College, Saidabad, Hyderabad, Telangana, India
  3. Professor, Department of Data Science And Business Systems ,School Of Computing, Srm Institute Of Science And Technology, Kattankulathur, Tamil Nadu, India

Abstract

Polymers are widely used in aerospace, automotive, biomedical, packaging, electronics, and manufacturing industries because of their lightweight nature, durability, and versatility. Accurate polymer identification and characterization are essential for quality control, recycling, performance assessment, and the development of advanced materials. Characterization helps determine important properties such as chemical composition, molecular structure, thermal stability, mechanical strength, and surface morphology, which influence material performance and application suitability.

Traditional polymer detection methods include Fourier Transform Infrared Spectroscopy (FTIR), Raman Spectroscopy, Nuclear Magnetic Resonance (NMR), Scanning Electron Microscopy (SEM), Differential Scanning Calorimetry (DSC), and Thermogravimetric Analysis (TGA). These techniques provide reliable and detailed information about polymer properties but often require expensive equipment, specialized expertise, and significant processing time.

Recent advances in computer-based analysis, machine learning, and artificial intelligence have significantly improved polymer characterization and classification. Intelligent algorithms can automatically identify polymer types, predict material properties, detect defects, and analyze complex datasets with high accuracy. Machine learning models such as Support Vector Machines, Random Forests, and Deep Neural Networks enable faster and more efficient analysis compared to conventional methods.

This survey reviews both traditional polymer detection techniques and emerging computational approaches. It discusses their working principles, advantages, limitations, and applications while highlighting current challenges and future research opportunities in AI-driven polymer analysis and material informatics.

Keywords: Nuclear Magnetic Resonance, machine learning, Vector Machines, polymeric materials and polymer characterization.

How to cite this article:
M. Vasavi, T. Prem Chander, Murugan Ambigapathy. A Comprehensive Survey of Polymer Detection Techniques and Computer-Based Analysis Methods for Advanced Material Characterization. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
M. Vasavi, T. Prem Chander, Murugan Ambigapathy. A Comprehensive Survey of Polymer Detection Techniques and Computer-Based Analysis Methods for Advanced Material Characterization. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=247499


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Ahead of Print Subscription Review Article
Volume 14
03
Received 15/06/2026
Accepted 22/06/2026
Published 24/06/2026
Publication Time 9 Days
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