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

4 publications

  • Published Subscription Original Research

    Develop the Design of Sustainable Polymer Materials: Applying Reinforcement Learning, IoT-Enabled Monitoring, and Data-Driven Manufacturing Approaches

    By C. Selvarathi, M. Parthiban, D. Pavunraj, D. Shobana, V. Parimala, R. Dhivya, S. Palpandi

    Abstract: Sustainable polymer materials development is a must due to resource constraints, environmental concerns, and the demand for designed materials with high performance. When it comes to material optimization, energy utilization, process unpredictability, and lifecycle sustainability, traditional polymer production methods have their challenges. Reinforcement Learning (RL), Internet of Things (IoT) monitoring, and data-driven production are utilized in the design and manufacturing of sustainable polymer materials. It is recommended to use Internet …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 Read article

  • Published Subscription Original Research

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

    By A. Geethapriya, Lakshmi Sridevi, M.V. Karthikeyan, D. Shobana, R. Prithviraj, A. Jeyamurugan, E. Anna Devi

    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 …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 169–192 Read article

  • Published Subscription Original Research

    Enhance Thermal and Conductive Properties through Graph Neural Network-Based Machine Learning-Driven Advanced Polymer Material Design

    By R. Kiruba Buri, L.C. Manikandan, D. Shobana, A. Jeyamurugan, V. Parimala, Veeraiyah Thangasamy, J. Jeya Caleb

    Abstract: Advanced polymer materials are widely used in modern engineering and manufacturing because of their lightweight nature, flexibility, durability, and adaptability to different applications. However, designing polymer materials with enhanced thermal and electrical properties remains a challenging task. The performance of polymers is influenced by a complex combination of molecular structures, filler materials, processing parameters, and nanoscale interactions. Conventional optimization methods often require extensive experimental trials and computational resources, making it …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 Read article

  • Published Subscription Original Research

    AI-Driven Framework for Accelerating Polymer Nanocomposite Commercialization in Computational Materials Engineering

    By M. Sukanya, Bantupalli Nagalakshmi, D. Shobana, V. Parimala, S. Ahamed Ali, K. Muthukannan, A. Thilagavathy

    Abstract: The remarkable mechanical strength increased functional qualities, lightweight structure, and thermal stability of polymer nanocomposites have prompted modern materials research to prioritize their rapid commercialization. Advanced materials can be created by adding nanoscale fillers such as carbon nanotubes, graphene, silica, and metal oxides to polymer matrices. These materials have applications in biomedical engineering, aerospace, electronics, packaging, and automobile manufacture. Research and development of polymer nanocomposites has traditionally relied on costly …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1–19 Read article

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