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

Journal of Polymer & Composites Original Research Special issue

AI-Enabled Optimization of Additively Manufactured Composite Materials for Enhanced Mechanical and Thermal Performance

  1. G. Nagaraj Department of Mechanical Engineering, Sethu Institute of Technology, Virudhunagar District
  2. P. Girish Department of Civil Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore
  3. Pallavi Hallappanavar Basavaraja Department of Information Science and Engineering, Academy of Technology and Management, Bangalore
  4. Anusha Preetham Department of Computer Science and Engineering, Dayanand Sagar College of Engineering, Bangalore
  5. G. Anil Kumar Department of Physics, Sreenidhi Institute of Science and Technology, Jawaharlal Nehru Technological University, Hyderabad
  6. D. Gouse Peera Department of Civil Engineering, Annamacharya University, Rajampet

Abstract

This paper discusses the optimization of multi-objective optimization of enhanced coupling of heat and mechanical properties of 3D printed polymer composite materials by artificial intelligence (AI), as a component of a multi-objective optimization framework. It aims at development of nonlinear printing parameters and material properties relationships to achieve maximum tensile strength and thermal conductivity in polymer composites produced through fused deposition modeling (FDM). Short carbon fiber reinforcement was used to make the polymer composite by FDM at various deposition temperatures, raster pattern, and infill densities. A machine-learning model was created experimentally with the tensile strength and thermal conductivity of the polymer composite measured and then combined with an evolutionary optimization algorithm to give a combination of printing parameters, which will give the optimum structural and thermal performance of the final part. The optimized composite specimens exhibited an average tensile strength improvement of approximately 9.4% compared to baseline systems reported in recent literature. Similarly, effective thermal conductivity increased by nearly 14.2%, indicating improved heat transfer pathways within the composite matrix. The predictive model achieved a coefficient of determination of 0.94 for tensile strength and 0.91 for thermal conductivity, demonstrating strong agreement between predicted and experimental responses. The suggested structure will allow the simultaneous enhancement of mechanical and thermal performance of additive-manufactured polymer composite structures, and, potentially, intelligent process-driven design of multifunctional composite elements.

Keywords

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