Development of Lightweight and Strong Composites Based on Machine-Learning Driven Structure and Process Designs

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Over the past decade, there has been a global increase in environmental awareness leading to an overwhelming need for lightweight polymer-based composites that possess demanding mechanical, physical, and functional characteristics that meet the standards of novel energy efficient "green" vehicles. This international collaboration will facilitate the development of advanced engineered composite materials by combining the expertise of the collaborators through a theoretical and experimental approach. This project aims to achieve the following objectives: (1) Develop various machine learning-based composite frameworks, to efficiently design and optimize polymer composite structures, according to the size of the design space and injection molding processes, and (2) Engineer and manufacture multifunctional lightweight composites with superior performances. In the design and manufacturing of advanced composite materials, it is critical to understand the influence of the materials and processing parameters on the fabricated composite's microstructures, as these structures will directly determine the inherent properties of these materials. Recent advancements in machine learning techniques have the potential to expedite the design and optimization processes associated with the manufacturing parameters that contribute to the composites' structure and properties. These techniques are extremely advantageous as they require minimal experimental data to develop an optimization framework. Implementing this approach will lead to the creation high-performance lightweight composites that can replace traditional materials, thereby increasing the energy efficiency of vehicles while lowering their overall environmental impact.