Cognitive AI-Based Quality Control and Operational Optimization of Polymer Composites for Healthcare Applications

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

    Vandana Ahuja,

  • Seemanthini K,

  • Sivabalakrishnan R,

  • Pradeep Kumar Sambamurthy,

  • Maddali Radha Madhavi,

  • Swarna Kuchibhotla,

  1. Professor, Department of Computer Science and Engineering, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana, India
  2. Associate Professor, Department of Machine Learning (AIML), B.M.S College of Engineering, Bengaluru, Karnataka, India
  3. Associate Professor, Department of Mechatronics Engineering, Bannari Amman Institute of Technology, Alathukombai PO, Sathyamangalam, Erode, Tamil Nadu, India
  4. Independent Researcher, Senior Member, IEEE, Atlanta, USA
  5. Associate Professor, Department of Mathematics, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, Andhra Pradesh, India
  6. Associate Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, Andhra Pradesh, India

Abstract

The use of polymer composite materials in healthcare is on the rise because of their adjustable mechanical characteristics, biocompatibility and structural flexibility. Yet, it is difficult to ensure stable quality of such composites due to process-related defects, heterogeneity of the material and the lack of real-time adaptive control. The proposed study suggests the use of cognitive AI-based framework of quality control and optimization of operation of polymer composite systems which are specifically aimed at healthcare-grade uses. The framework combines convolutional neural networks used to detect defects, the use of XGBoost-based regression to predict the quality, a reinforcement learning model to optimize the adaptive process, and a digital twin to simulate the process in real-time. The model development and validation were done using a multi-modal dataset, consisting of material properties, processing conditions, and defect characteristics. The accuracy of the proposed system in detecting defects was about 97% and there were great improvements in quality prediction accuracy and process efficiency. Markedly, the reinforcement learning module allowed adjusting curing parameters dynamically, which led to a decrease in the rate of defects and improved the performance of composite. Comparative analysis shows the proposed framework to be better than the traditional machine learning and inspection-based ones because it allows the closed-loop and adaptive control of manufacturing. The results indicate the possibility of using cognitive AI together with polymer composite material production to achieve the healthcare standard of reliability. This direction provides a roadmap to scalable production of intelligent, defect-aware, and efficient production of advanced composite in biomedical and industrial use.

Keywords: Cognitive AI, Polymer composites, Quality control, Reinforcement learning optimization, Healthcare materials.

How to cite this article:
Vandana Ahuja, Seemanthini K, Sivabalakrishnan R, Pradeep Kumar Sambamurthy, Maddali Radha Madhavi, Swarna Kuchibhotla. Cognitive AI-Based Quality Control and Operational Optimization of Polymer Composites for Healthcare Applications. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
Vandana Ahuja, Seemanthini K, Sivabalakrishnan R, Pradeep Kumar Sambamurthy, Maddali Radha Madhavi, Swarna Kuchibhotla. Cognitive AI-Based Quality Control and Operational Optimization of Polymer Composites for Healthcare Applications. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=244125


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Ahead of Print Subscription Original Research
Volume 14
03
Received 12/04/2026
Accepted 02/05/2026
Published 16/05/2026
Publication Time 34 Days
22

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