Optimization of Lightweight Polymer Composites Using Finite Element Analysis Machine Learning and Topology Optimization Techniques for Aerospace Applications

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Year : 2025 | Volume : 13 | 06 | Page :
    By

    A. Arunkumar,

  • Jyothhi yarlagaddaa,

  • I Sreevani,

  • Boopathy G,

  • Mohit Tiwari,

  • Avinash Kumar,

  • L. Ganesh Babu,

  • T. Venkatajalapathi,

  1. Assistant Professor, Department of Mechanical Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, India
  2. Associate Professor, Department of Mechanical Engineering, vignan foundation for science, technology and research, Guntur, Andhra Pradesh, India
  3. Professor, Department of Humanities and Sciences, K.S.R.M. College of Engineering, Kadapa, Andhra Pradesh, India
  4. Professor, Department of Aeronautical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
  5. Assistant Professor, Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, Delhi, India
  6. Assistant Professor, Department of Mechanical Engineering, Cambridge Institute of Technology, Ranchi, Jharkhand, India
  7. Assistant Professor (SG), Department of Robotics and Automation, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India
  8. Associate Professor, Department of Mechanical Engineering, V. S. B. College of Engineering Technical Campus, Coimbatore, Tamil Nadu, India

Abstract

The advancement of aerospace engineering depends on lightweight polymer matrix composites (PMCs) because they help decrease weight while improving fuel efficiency and payload capacity together with increased structural integrity. Research developed a computer program comprising FEA with ANN and TO optimize high-performance PMCs through integrated design approaches. The combination of Python-controlled LS-DYNA simulations measured hybrid composite laminate resistance to impact while an ANN model obtained data from simulations to forecast projectile velocities across different reinforcement patterns. The utilization of TO allowed researchers to reduce material mass by 28% while maintaining 92% of stiffness properties and increasing energy absorption by 25%. The research established that Kevlar/Epoxy and hybrid composite materials outperform other options as aerospace blocking components due to their excellent ability to resist impacts. The study proves that using computational intelligence and optimization techniques will produce effective aerospace-grade composites in the next generation.

Keywords: Polymer Matrix Composites, Finite Element Analysis, Artificial Neural Networks, Topology Optimization, Impact Resistance.

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How to cite this article:
A. Arunkumar, Jyothhi yarlagaddaa, I Sreevani, Boopathy G, Mohit Tiwari, Avinash Kumar, L. Ganesh Babu, T. Venkatajalapathi. Optimization of Lightweight Polymer Composites Using Finite Element Analysis Machine Learning and Topology Optimization Techniques for Aerospace Applications. Journal of Polymer and Composites. 2025; 13(06):-.
How to cite this URL:
A. Arunkumar, Jyothhi yarlagaddaa, I Sreevani, Boopathy G, Mohit Tiwari, Avinash Kumar, L. Ganesh Babu, T. Venkatajalapathi. Optimization of Lightweight Polymer Composites Using Finite Element Analysis Machine Learning and Topology Optimization Techniques for Aerospace Applications. Journal of Polymer and Composites. 2025; 13(06):-. Available from: https://journals.stmjournals.com/jopc/article=2025/view=228960


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Ahead of Print Subscription Original Research
Volume 13
06
Received 15/07/2025
Accepted 09/08/2025
Published 09/10/2025
Publication Time 86 Days
110

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