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

Journal of Polymer & Composites Original Research

Federated Learning Framework for Sustainable Multi-Scale Design of Recyclable Thermoplastic Graphene Composites in Smart Manufacturing Environments

  1. Krishna Priya R Faculty of Engineering, University of Technology and Applied Sciences, Musandam, PO. 12, PC 811, Khasab, Musandam
  2. R. Dhanasekar Department of Electrical and Electronics Engineering Sri Sairam Engineering College, Chennai
  3. A. Saravanan Department of Automobile Engineering Kongu Engineering College
  4. A Ramaprathap Reddy Department of Artificial Intelligence & Machine Learning (Computer Science and Engineering), R.V.R. & JC College of Engineering, Chaudavaram, Guntur
  5. Ramesh Velumayil Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai

Abstract

The growing demand for sustainable advanced materials has accelerated the development of recyclable thermoplastic graphene composites for next-generation smart manufacturing systems. The typical central optimization methods have challenges with data privacy, scalability, and poor collaboration between distributed manufacturing sites. By combining material informatics, edge intelligence and distributed artificial intelligence, this study introduces a Federated Learning (FL) framework to design recyclable thermoplastic graphene composites at multiple scales sustainably. The proposed framework allows multiple manufacturing nodes to use them to train their own predictive models without revealing any proprietary process or material information, ensuring confidentiality while enhancing the model's generalization. These multi-scale material descriptors – such as graphene dispersion, interface bonding, crystallinity, fiber orientation, tensile strength, thermal conductivity, recyclability index, and energy consumption – are integrated in the federated optimization process to ensure structure–property relationships. Smart manufacturing sensors continuously collect production data in real time, and through adaptive model updates, it can improve the stability of the production process, reduce the waste of production materials, and optimize the manufacturing efficiency. The framework also includes subjective sustainability functions, to optimize mechanical performance and recyclability while reducing carbon emissions and production costs. Through simulation-based evaluation, it is shown that the system enables significant improvements in terms of accuracy of predictions, stability of convergence, use of resources and sustainability of the lifecycle with respect to traditional centralized machine learning approaches. This proposed federated intelligence approach offers a safe, scalable and eco-friendly approach to designing high-performance recyclable thermoplastic graphene composites for smart manufacturing in an Industry 5.0 context.

Keywords

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