A MULTI-OBJECTIVE OPTIMIZATION APPROACH FOR DESIGNING SUSTAINABLE MATERIALS FOR RECYCLED MATERIALS USING AI

Designing sustainable materials that incorporate recycled materials is crucial for reducing environmental impacts and promoting circular economy principles. Traditional approaches to material design often rely on trial-and-error methods and are time-consuming and resource-intensive. This abstract presents a multi-objective optimization approach for designing sustainable materials using artificial intelligence (AI) techniques. The proposed approach leverages AI algorithms, such as machine learning and genetic algorithms, to optimize the composition and properties of materials that incorporate recycled materials. Multiple objectives, including mechanical performance, environmental impact, and cost, are considered to achieve a well-balanced sustainable material design. The AI algorithms analyse vast amounts of data on the properties and characteristics of recycled materials, as well as desired material performance criteria, to generate optimal material formulations. By utilizing AI techniques, the multi-objective optimization approach overcomes the limitations of traditional methods and enables the exploration of a wider design space. It facilitates the identification of optimal material compositions that meet desired performance requirements while minimizing environmental impacts and maximizing the utilization of recycled materials. The approach also considers the economic feasibility by assessing the cost-effectiveness of the designed materials. The effectiveness of the approach is demonstrated through case studies, where different material formulations are optimized using the proposed multi-objective optimization framework. The results indicate that the AI-based approach can efficiently identify sustainable material designs that outperform traditional materials in terms of mechanical properties and environmental sustainability. It enables the consideration of multiple criteria and trade-offs in material design, ensuring that sustainable materials meet performance requirements while addressing environmental concerns. In multi-objective optimization approach for designing sustainable materials using AI techniques. By integrating machine learning and genetic algorithms, the approach enables the efficient design of materials that incorporate recycled materials while balancing mechanical performance, environmental impact, and cost considerations. The results demonstrate the potential of AI-based approaches to revolutionize sustainable material design and contribute to the advancement of circular economy principles.