STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Polymer & Composites Original Research Special issue

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

  1. Vandana Ahuja Department of Computer Science and Engineering, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala
  2. Seemanthini K. Department of Machine Learning (AIML), B.M.S College of Engineering, Bengaluru
  3. Sivabalakrishnan R. Department of Mechatronics Engineering, Bannari Amman Institute of Technology, Alathukombai PO, Sathyamangalam, Erode
  4. Pradeep Kumar Sambamurthy Senior Member, IEEE
  5. Maddali Radha Madhavi Department of Mathematics, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District
  6. Swarna Kuchibhotla Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District

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

References (30)

  1. Karuppusamy, R. Thirumalaisamy, S. Palanisamy, S. Nagamalai, E. E. S. Massoud, and N. Ayrilmis, “A review of machine learning applications in polymer composites: Advancements, challenges, and future prospects,” Journal of Materials Chemistry A, vol. 13, no. 22, 2025, doi:10.1039/D5TA09634K.
  2. Wu Z, Yang Y, Hao J, Soutis C. Optimization and integration of polymer composites manufacturing powered by artificial intelligence. Frontiers of Chemical Science and Engineering. 2025;19(12). doi:10.1007/s11705-026-2637-7
  3. Nikooharf MH, Shirinbayan M, Arabkoohi M, Bahlouli N, Fitoussi J, Benfriha K. Machine learning in polymer additive manufacturing: a review. International Journal of Material Forming. 2024;17(6). doi:10.1007/s12289-024-01854-8
  4. Mamodiya U, Kishor I, Reddy PS, Kalpana KL, Seelaboyina R, Gantla HR. Machine Learning-Enhanced Multiscale Computational Framework for Optimizing Thermoelectric Performance in Nanostructured Materials. Computers, Materials & Continua. 2026;87(3):1-10. doi:10.32604/cmc.2026.076464
  5. Omidian H. AI-powered breakthroughs in material science and biomedical polymers. Journal of Bioactive and Compatible Polymers. 2024;40(2):161-174. doi:10.1177/08839115241308202
  6. Kennedy SM, K A, K P, RB JR. Artificial intelligence and machine learning-driven design of self-healing biomedical composites. Expert Review of Medical Devices. 2025;22(8):787-805. doi:10.1080/17434440.2025.2520291
  7. Kishor I, Maniraj SP, Syed AA, Mamodiya U, Kumawat V. Next-Generation Medical Solutions With AI-Powered Haptics. Advances in Computational Intelligence and Robotics. 2025:351-374. doi:10.4018/979-8-3373-2307-7.ch016
  8. Sharma, T. Mukhopadhyay, S. M. Rangappa, S. Siengchin, and V. Kushvaha, “Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design,” Jul. 2024, doi:10.60692/42w29-acv28.
  9. Malashin I, Martysyuk D, Tynchenko V, Gantimurov A, Semikolenov A, Nelyub V, et al. Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review. Polymers. 2024;16(23):3368. doi:10.3390/polym16233368
  10. Mamodiya U, Kishor I. Medical robotics for healthcare management: Transforming patient care through automation and AI. Emerging Trends in Medical Robotics. 2026:173-204. doi:10.1016/b978-0-443-33028-5.00004-5
  11. Zubayer MH, Xiong Y, Wang Y, Imdadul HM. Enhancing additive manufacturing precision: Intelligent inspection and optimization for defect-free continuous carbon fiber-reinforced polymer. Composites Part C: Open Access. 2024;14:100451. doi:10.1016/j.jcomc.2024.100451
  12. Rabby MM, Das PP, Rahman M, Vadlamudi V, Raihan R. Fast and accurate prediction of cure quality and mechanical performance in fiber‐reinforced polymer composite using dielectric variables and machine learning. Polymer Composites. 2023;45(2):1810-1825. doi:10.1002/pc.27891
  13. Kishor I, Mamodiya U, Ahmad SS, Goyal P, Dwivedi D. Enabling Smarter Nanosystems. Enhancing Hybrid Nanodevice Fabrication Efficiency Using Machine Learning. 2026:113-137. doi:10.1002/9781394355310.ch6
  14. Li X, Lu Z, Zeng C. Robotic and intelligent technologies in composite material inspection: a review. Robot Learning. 2024. doi:10.55092/rl20240005
  15. Kishor I, Mamodiya U, Ahmad SS, Goyal P, Dwivedi D. Harnessing Unsupervised Machine Learning for Advanced Nanodevice Fabrication. Enhancing Hybrid Nanodevice Fabrication Efficiency Using Machine Learning. 2026:139-158. doi:10.1002/9781394355310.ch7
  16. Li H, Wang Q, Hong X. Self-Healing MXene/Polymer Composites for Healthcare Applications. Fibers and Polymers. 2024;25(10):3601-3621. doi:10.1007/s12221-024-00658-6
  17. Aboamer MA, Hakami A, Algethami M, Alarifi IM, El-Bagory TMAA, Alassaf A, et al. Sustainable Medical Materials: AI-Driven Assessment for Mechanical Performance of UVC-Treated Date Palm Epoxy Composites. Polymers. 2025;17(8):1125. doi:10.3390/polym17081125
  18. Das PP, Elenchezhian M, Vadlamudi V, Raihan R. Artificial Intelligence Assisted Residual Strength and Life Prediction of Fiber Reinforced Polymer Composites. AIAA SCITECH 2023 Forum. 2023. doi:10.2514/6.2023-0773
  19. M’Bengue MS, Mesnard T, Chai F, Maton M, Gaucher V, Tabary N, et al. Evaluation of a Medical Grade Thermoplastic Polyurethane for the Manufacture of an Implantable Medical Device: The Impact of FDM 3D-Printing and Gamma Sterilization. Pharmaceutics. 2023;15(2):456. doi:10.3390/pharmaceutics15020456
  20. Mamodiya U, Kishor I, Jigyasu R, Ghai M, Kakarla NML. Explainable Quantum-Neuromorphic Intelligence. Advances in Computational Intelligence and Robotics. 2026:333-366. doi:10.4018/979-8-3373-7779-7.ch011
  21. Rojek I, Dostatni E, Kopowski J, Macko M, Mikołajewski D. AI-Based Support System for Monitoring the Quality of a Product within Industry 4.0 Paradigm. Sensors. 2022;22(21):8107. doi:10.3390/s22218107
  22. Khinvasara T, Ness S, Shankar A. Leveraging AI for Enhanced Quality Assurance in Medical Device Manufacturing. Asian Journal of Research in Computer Science. 2024;17(6):13-35. doi:10.9734/ajrcos/2024/v17i6454
  23. University of Toulouse, Kpoghomou R, Nassiet V, University of Toulouse, Kamsu-Foguem B, Tarbes Technological University. AI Application Potential and Prospects in Materials Science: A Focus on Polymers. Chronicle of Computing. 2025. doi:10.55432/978-1-6692-0011-6_2
  24. Cassola S, Duhovic M, Schmidt T, May D. Machine learning for polymer composites process simulation – a review. Composites Part B: Engineering. 2022;246:110208. doi:10.1016/j.compositesb.2022.110208
  25. Uddin MH, Mulla MH, Abedin T, Manap A, Yap BK, Rajamony RK, et al. Advances in natural fiber polymer and PLA composites through artificial intelligence and machine learning integration. Journal of Polymer Research. 2025;32(3). doi:10.1007/s10965-025-04282-7
  26. Adaptive Automation and Defect Control in Forming Processes of Composite Materials: A Robust Parametrisation Approach to Improve Simulation Accuracy,” May 2025, doi:10.25916/sut.29036666.
  27. Khan I, Al Rashid A, Koç M. Integration of machine learning and digital twin in additive manufacturing of polymeric-based materials and products. Progress in Additive Manufacturing. 2025;10(12):10685-10737. doi:10.1007/s40964-025-01257-4
  28. Akrache R, Mohammed Ridha Shakir W, Charafeddine J. Enhancing Prosthetic and Orthopedic Support Devices Through Advanced Composite Material Assembly and Design Optimization. Volume 4: Biomedical and Biotechnology Engineering. 2024. doi:10.1115/imece2024-147261
  29. Palanisamy S, Murugesan TM, Palaniappan M, Santulli C, Ayrilmis N. Fostering sustainability: The environmental advantages of natural fiber composite materials – a mini review. Environmental Research and Technology. 2024;7(2):256-269. doi:10.35208/ert.1397380
  30. Palanisamy S, Kalimuthu M, Azeez A, Palaniappan M, Dharmalingam S, Nagarajan R, et al. Wear Properties and Post-Moisture Absorption Mechanical Behavior of Kenaf/Banana-Fiber-Reinforced Epoxy Composites. Fibers. 2022;10(4):32. doi:10.3390/fib10040032
Support