International Research Journal of Education and Technology

The automotive industry increasingly relies on composite materials to enhance the performance and efficiency of critical components. This project focuses on developing an AI-driven methodology to optimize composite materials for brake drums, with the aim of achieving superior performance, cost-effectiveness, and seamless integration into manufacturing processes.Leveraging advanced machine learning algorithms and material science principles, this research seeks to identify the ideal composition and structural characteristics of composite materials. AI models are trained on extensive datasets, including material properties, thermal behavior, wear resistance, and manufacturing parameters. This approach enables the prediction of performance metrics under various operating conditions, ensuring optimal material selection.

This integrates multi-objective optimization techniques to balance competing factors, such as cost, durability, and environmental impact. By simulating real-world scenarios, the AI system can recommend composite material configurations tailored to specific automotive requirements.

Furthermore it explores innovative manufacturing integration strategies. This includes leveraging AI to optimize production techniques such as additive manufacturing and automated assembly lines, reducing waste and production costs while maintaining high-quality standards.The outcomes are expected to revolutionize brake drum design by delivering lighter, more durable, and cost-efficient components. Additionally, the methodology can be extended to other automotive applications, fostering sustainability and innovation across the industry.

This AI-driven framework aligns with the future of smart manufacturing, setting a precedent for how AI can transform material science and automotive engineering.

Key Words: AI-driven material optimization,Composite materials,Brake drum design.Machine learning in material science,Automotive engineering,Sustainable

manufacturing,Multi-objective optimization,Performance enhancement,Cost-effective materials