Q-Learning Driven Metaheuristic for Tailoring the Solid State Properties of Carbon Nanotube-Graphene Nanocomposites for Automotive and Aerospace Applications

The implementation of deep learning strategies has transformed composite material design and allowed the optimization of carbon fibre with epoxy resin beyond previous limits for greater performance results. Developing dependable structure-property-performance links continues to present a major obstacle because these systems operate through intricate combinations between nanotech advances and manufacturing simulations. This study presents a multi-faceted methodology employing a reinforcement learning (RL) approach to optimize carbon fiber and epoxy resin-based composites, incorporating over 10,000 samples of carbon nanotube, graphene, and polymer matrix combinations, thus aiming to achieve specific performance criteria such as tensile strength and thermal conductivity. Utilizing Langmuir-Blodgett self-assembly and Electrospinning methodologies, the research integrates novel nanotechnology to fabricate and test the designed composites. Combining RL with advanced manufacturing and finite element analysis simulation techniques provides a holistic view of the structure-property-performance relations in composite materials. Through iterative optimization, the expected outcomes indicate a potential improvement in mechanical properties by 15%–25% and thermal performance by 10%–20%. Simulated thermal expansion coefficient distributions show that the proposed model consistently achieves values in the range of 1.8 to 3.0 in terms of frequency and reliability. The outcomes of this research have significant implications for aerospace and automotive sectors.