Develop Intelligent Polymer Composites Utilizing Machine Learning and Generative AI: Enhancing Mechanical, Thermal, and Environmental Effectiveness

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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 | 05 | Page :
By

S. Aiswarya,

D. Sheema,

G. Rajani Reddy,

K. Padmapriya,

S. Ganesan,

L. Manimegalai,

R. RoselinKiruba,

  1. Assistant Professor, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
  2. Associate Professor, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
  3. Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
  4. Associate Professor, Department of Computer Science and Engineering, R. M. D Engineering College, Kavaraipettai, Tamil Nadu, India
  5. Assistant Professor, Department of Computer Science and Business Systems, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India
  6. Assistant Professor, Department of Electronics and Communication Engineering, R.M.K. College of Engineering and Technology, Kavaraipettai, Tamil Nadu, India
  7. Associate Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India

Abstract

The design of novel polymeric materials increasingly demands integrated methodologies that may concurrently improve structural performance, thermal stability and environmental sustainability. Here we provide an intelligent materials development platform that integrates data-driven learning approaches and generative artificial intelligence to speed the design, optimization and assessment of polymer composite systems. Predictive models are taught with experimental and simulated data sets including material composition, reinforcement characteristics, processing circumstances, mechanical properties, temperature responses and environmental indicators. Machine learning algorithms uncover complicated correlations between the material parameters and the performance results, generative artificial intelligence studies possible material arrangements and offers the best combinations of constituents and processing circumstances. The main response components evaluated in the suggested framework are tensile strength, elastic modulus, impact resistance, thermal stability, degradation behavior, material efficiency and environmental performance. Multi-objective optimization is used to optimize the trade-off between mechanical durability, thermal resistance, resource efficiency and ecological effect. The integrated strategy can reduce the dependence on large scale trial-and-error experimentations, shorten the cycle of material creation and boost the reliability of forecast. The feedback loop between experimental results and computer projections also allows us to constantly optimize material formulations. The suggested technology offers a scalable strategy for the creation of high performance and eco-friendly composite materials which enables sustainable production and application specific material choice in automotive, aerospace, construction, energy and other engineering sectors.

Keywords: Predictive modeling, Thermal stability, Mechanical durability, Eco-efficiency, Material formulation, Sustainable manufacturing, Multi-objective optimization.

How to cite this article: S. Aiswarya, D. Sheema, G. Rajani Reddy, K. Padmapriya, S. Ganesan, L. Manimegalai, R. RoselinKiruba. Develop Intelligent Polymer Composites Utilizing Machine Learning and Generative AI: Enhancing Mechanical, Thermal, and Environmental Effectiveness. Journal of Polymer & Composites. 2026; 14(05):-.
How to cite this URL: S. Aiswarya, D. Sheema, G. Rajani Reddy, K. Padmapriya, S. Ganesan, L. Manimegalai, R. RoselinKiruba. Develop Intelligent Polymer Composites Utilizing Machine Learning and Generative AI: Enhancing Mechanical, Thermal, and Environmental Effectiveness. Journal of Polymer & Composites. 2026; 14(05):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=262330

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Ahead of Print Subscription Original Research
Volume 14
05
Received 10/10/2026
Accepted 23/10/2026
Published 02/11/2026
Publication Time 23 Days

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