The Convergence of AI and Composites – A Review Anchored in Patent Trends

Year : 2025 | Volume : 13 | Issue : 06 | Page : 182 198
    By

    Silambarasan B.,

  • Gayathiri N.R.,

  • Thirumaraiselvi C.,

  • Mahendra Boopathi M.,

  • Hariprasad P.,

  • Gokulkumar S.,

  1. Assistant Professor, Department of Mechanical Engineering, Sir Issac Newton College of Engineering and Technology, Nagapattinam, Tamil Nadu, India
  2. Professor, Department of Computer Science Engineering, Christ the King Engineering College, Coimbatore, Tamil Nadu, India
  3. Professor, Department of Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India
  4. Professor, Department of Mechanical Engineering, CMS College of Engineering and Technology, Coimbatore, Tamil Nadu, India
  5. Assistant Professor, Department of Mechanical Engineering, KIT -Kalaignar Karunanidhi Institute of Technology, Coimbatore, Tamil Nadu, India
  6. Assistant Professor, Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India

Abstract

The integration of artificial intelligence (AI) and machine learning (ML) techniques is revolutionizing the design, analysis, and optimization of polymer (PC/FRP), metal (MC), and ceramic matrix composites (CC). Techniques such as artificial neural networks (ANN), deep learning (DL), genetic algorithms (GA), and physics-informed machine learning (PIML) are employed to enhance property estimation, process optimization, and predictive modeling. These AI-driven frameworks enable virtual testing, application-specific material design, and real-time decision-making, while reducing computational and experimental burdens. Key applications span sectors such as aerospace, electronics, biomedicine, and advanced manufacturing. AI and ML approaches are being used to improve mechanical properties, including tensile strength, wear resistance, and thermal stability, as well as to optimize machining parameters and predict the fatigue life. The utilization of decision-making algorithms and advanced algorithms, such as convolutional neural networks (CNNs), allows for predictive maintenance, quality assurance, and real-time monitoring during fabrication. In parallel, global patent activity shows a steady rise, led by the United States, with increasing contributions from the emerging economies. Prominent IPC classes, such as G06F (electrical digital data processing), H01L (semiconductor devices), and G01N (investigating materials), reflect the growing innovations in computing, biotechnology, and material analysis. Together, these trends mark a paradigm shift towards intelligent, data-driven composite material development and innovation on a global scale, ushering in a new era of smart, efficient, and high-performance composites for advanced applications

Keywords: Composite materials, Artificial intelligence, Machine learning, Neural networks, Patent landscape, Deep learning, Predictive modeling

[This article belongs to Journal of Polymer and Composites ]

How to cite this article:
Silambarasan B., Gayathiri N.R., Thirumaraiselvi C., Mahendra Boopathi M., Hariprasad P., Gokulkumar S.. The Convergence of AI and Composites – A Review Anchored in Patent Trends. Journal of Polymer and Composites. 2025; 13(06):182-198.
How to cite this URL:
Silambarasan B., Gayathiri N.R., Thirumaraiselvi C., Mahendra Boopathi M., Hariprasad P., Gokulkumar S.. The Convergence of AI and Composites – A Review Anchored in Patent Trends. Journal of Polymer and Composites. 2025; 13(06):182-198. Available from: https://journals.stmjournals.com/jopc/article=2025/view=232799


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Regular Issue Subscription Review Article
Volume 13
Issue 06
Received 25/08/2025
Accepted 03/09/2025
Published 13/10/2025
Publication Time 49 Days
20

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