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

3 publications

  • Published Subscription Original Research Special issue

    Explainable Machine Learning for Process Parameter Optimization in Gradient 3D-Printed Polymer Composites

    By Harish Reddy Gantla, Harish Chandra Mohanta, Deepika Singh Singraur, Sandeep Bansal, Varinder Singh, Pacha Supriya

    Abstract: The explainable machine learning-based structure may be employed to achieve a favorable process parameter of the graduate 3D-printed polymer composite structures to improve the mechanical and thermal properties without compromising the transparency of the decisions made during the fabrication process. Gradient composite specimens were made by systematically varied process parameters like nozzle temperature, raster orientation, deposition speed, gradient transition rate and fused filament fabrication. A predictive model of tensile strength …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 847–866 Read article

  • Published Subscription Original Research Special issue

    Machine Learning-Driven Polymer Composite Smart Skin for Integrated Sensing in Soft Robotic Systems

    By Harish Reddy Gantla, Radha Seelaboyina, M. Sreenivasulu, S. Sagar Imambi, Sanjeev Kumar, Muthukumar Subramanian

    Abstract: Soft robotics has grown rapidly, but its progress is still constrained by the limitations of current sensing skins. Most polymer-based sensors provide either flexibility or sensitivity, yet they struggle to deliver real-time communication and adaptive intelligence when deployed in complex robotic environments. This disconnect between material performance and system-level responsiveness forms a critical bottleneck for practical deployment. Existing approaches often treat tactile sensing and wireless communication as separate problems. As …

    Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 121–136 Read article

  • Published Subscription Original Research

    Data-Driven Life Prediction of Fiber-Reinforced Polymer Composites Using IoT Sensing and Machine Learning Algorithms

    By Harish Reddy Gantla, Kasthuri Rajendra Prasad, Anirbit Sengupta, Biyyapu Vishnu Priya, Manna Sheela Rani Chetty, Parul Goyal

    Abstract: The accurate prediction of fatigue life in fiber-reinforced polymer (FRP) composites remains a major challenge due to their nonlinear, multi-mechanism degradation behavior under variable loading conditions. This study presents a data-driven framework, H-LiProNet, which combines real-time IoT sensing with hybrid machine learning to estimate remaining useful life (RUL) in FRP composites. The proposed system integrates embedded Fiber Bragg Grating (FBG) and acoustic emission (AE) sensors to capture strain and damage …

    Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 116–130 Read article

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