Integrate AI and IoT to Develop Sustainable Polymer Structural Materials Processing Optimization: Enabled Monitoring Strategies for Performance and Lifecycle Assessment

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Year : 2026 | Volume : 14 | 04 | Page :
    By

    A. Geethapriya,

  • Lakshmi Sridevi,

  • M.V. Karthikeyan,

  • D. Shobana,

  • R. Prithviraj,

  • A. Jeyamurugan,

  • E. Anna Devi,

  1. Associate Professor, Department of Computer Science and Engineering, Misrimal Navajee Munoth Jain Engineering College, Thoraippakkam, Chennai, Tamil Nadu, India
  2. Professor, Department of Computer Science and Engineering, Chennai Institute of technology, Chennai, Tamil Nadu, India
  3. Professor, Department of Electronics and Communication Engineering, St. Joseph’s institute of technology, Chennai 119, Tamil Nadu, India
  4. Assistant Professor (S.G.), Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India
  5. Assistant Professor, Department of Artificial Intelligence (AI) and Data Science, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
  6. Assistant Professor (Senior Grade), Department of Artificial Intelligence and Data Science, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India
  7. Associate Professor, Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, Chengalpatu, Tamil Nadu, India

Abstract

The need for long-lasting structural polymer materials that are both environmentally friendly and highly mechanically effective is driving demand for these materials as the industrial sector continues to grow. Optimizing processes, saving energy, detecting faults, and monitoring structures are all hindered by conventional polymer manufacture. This study suggests an AI-IoT system for environmentally friendly production of structural polymer materials to get around these problems. Tools for evaluating system performance and lifespan have been enhanced. Internet of Things (IoT) sensors, real-time data collection, analytics on the cloud, and AI-driven prediction models all work together to make the framework more precise in production and more sustainable in operations. During the production of polymers, smart sensors keep tabs on a variety of parameters, including environmental factors, curing time, tensile strength, deformation, and more. Algorithms based on machine learning can enhance energy efficiency, decrease industrial waste, predict when materials will degrade, and identify process problems. Ensuring structural integrity and product quality is achieved through the integration of modern feedback control systems, which dynamically adjust processing conditions and make adaptive judgments. More resilient polymer structural components can have their failure and lifespan predicted by predictive maintenance solutions powered by artificial intelligence. The modules that assess a product’s lifetime take into account its environmental effect, resource consumption, carbon emissions, and recycling rates.

 

Keywords: Sustainable polymer materials, structural materials processing, lifecycle assessment, predictive maintenance, smart manufacturing, performance monitoring

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How to cite this article:
A. Geethapriya, Lakshmi Sridevi, M.V. Karthikeyan, D. Shobana, R. Prithviraj, A. Jeyamurugan, E. Anna Devi. Integrate AI and IoT to Develop Sustainable Polymer Structural Materials Processing Optimization: Enabled Monitoring Strategies for Performance and Lifecycle Assessment. Journal of Polymer & Composites. 2026; 14(04):-.
How to cite this URL:
A. Geethapriya, Lakshmi Sridevi, M.V. Karthikeyan, D. Shobana, R. Prithviraj, A. Jeyamurugan, E. Anna Devi. Integrate AI and IoT to Develop Sustainable Polymer Structural Materials Processing Optimization: Enabled Monitoring Strategies for Performance and Lifecycle Assessment. Journal of Polymer & Composites. 2026; 14(04):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=247509


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Ahead of Print Subscription Original Research
Volume 14
04
Received 25/05/2026
Accepted 02/06/2026
Published 24/06/2026
Publication Time 30 Days
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