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

Journal of Polymer & Composites Original Research

Machine Learning-Assisted Design and Optimization of Lightweight Polymer Composites for IoT-Enabled Automotive Applications

  1. Harish Reddy Gantla Department of Computer Science and Engineering, Vignan Institute of Technology and Science, Hyderabad
  2. Praveena Murala Department of Computer Science and Engineering, QIS College of Engineering and Technology
  3. Hameed Miyan School of Electrical and Communication Science, Department of Electronics and Telecommunication Engineering, JSPM University, Pune
  4. Prince Sood Department of Computer Science and Engineering, Swami Vivekananda Institute of Engineering and Technology, Ramnagar
  5. Suvidha Department of Computer Science and Engineering, Swami Vivekananda Institute of Engineering and Technology, Ramnagar
  6. C. M. Sheela Rani Department of Computer Science and Engineering, K L University, Green Fields, Vaddeswaram, Guntur

Abstract

This study aims to develop an integrated machine learning and optimization framework for the intelligent design of lightweight polymer composites suited for IoT-enabled automotive applications. The goal is to enhance material performance while satisfying multiple design constraints such as mechanical strength, thermal stability, and process compatibility. A curated dataset of polymer composite formulations was used to train a Random Forest Regression (RFR) model capable of predicting tensile strength, thermal conductivity, and density based on filler-matrix composition and processing parameters. A Genetic Algorithm (GA) was incorporated for multi-objective optimization using a composite-specific fitness function. The optimized formulations were validated through finite element simulation under ASTM D638 conditions. The RFR model achieved high accuracy with an R² of 0.93 for tensile strength prediction. The GA converged rapidly, identifying formulations that improved tensile strength by 22.4% and maintained high elongation at break. Comparative analysis showed the proposed ML-GA framework outperformed SVR, DNN, and Linear Regression in both accuracy and robustness. The optimized material system met structural, thermal, and functional criteria for automotive IoT components. This work introduces a closed-loop, data-driven design pipeline that integrates machine learning, evolutionary optimization, and application-level validation. Unlike prior empirical or single-objective approaches, this methodology is scalable, multi-objective, and application-aware, representing a significant advancement in polymer composite engineering for smart mobility.

Keywords

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