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

Year : 2025 | Volume : 13 | Issue : 05 | Page : 12 27
    By

    Harish Reddy Gantla,

  • Praveena Murala,

  • Hameed Miyan,

  • Prince Sood,

  • Suvidha,

  • C. M. Sheela Rani,

  1. Associate Professor, Department of Computer Science and Engineering, Vignan Institute of Technology and Science, Hyderabad, Telangana, India
  2. Assistant Professor, Department of Computer Science and Engineering, QIS College of Engineering and Technology, Andhra Pradesh, India
  3. Associate Professor, School of Electrical and Communication Science, Department of Electronics and Telecommunication Engineering, JSPM University, Pune, Maharashtra, India
  4. Assistant Professor, Department of Computer Science and Engineering, Swami Vivekananda Institute of Engineering and Technology, Ramnagar, Punjab, India
  5. Assistant Professor, Department of Computer Science and Engineering, Swami Vivekananda Institute of Engineering and Technology, Ramnagar, Punjab, India
  6. Professor, Department of Computer Science and Engineering, K L University, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, India

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: Polymer composites; machine learning; genetic algorithm; random forest; multi-objective optimization; IoT-integrated materials; automotive applications; RGO-reinforced composites; materials informatics; finite element validation

[This article belongs to Journal of Polymer and Composites ]

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How to cite this article:
Harish Reddy Gantla, Praveena Murala, Hameed Miyan, Prince Sood, Suvidha, C. M. Sheela Rani. Machine Learning-Assisted Design and Optimization of Lightweight Polymer Composites for IoT-Enabled Automotive Applications. Journal of Polymer and Composites. 2025; 13(05):12-27.
How to cite this URL:
Harish Reddy Gantla, Praveena Murala, Hameed Miyan, Prince Sood, Suvidha, C. M. Sheela Rani. Machine Learning-Assisted Design and Optimization of Lightweight Polymer Composites for IoT-Enabled Automotive Applications. Journal of Polymer and Composites. 2025; 13(05):12-27. Available from: https://journals.stmjournals.com/jopc/article=2025/view=222131


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Regular Issue Subscription Original Research
Volume 13
Issue 05
Received 21/05/2025
Accepted 28/05/2025
Published 22/07/2025
Publication Time 62 Days
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