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

基于机器学习驱动结构和工艺设计的轻质高强度复合材料的开发

• 批准号: 571704-2021
• 负责人: Lee, PatrickChangDongPCD
• 金额: $ 1.82万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759577
• 关键词: Development; Lightweight; Strong; Composites; Based

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.

在过去的十年中，全球环境意识的提高导致对轻质聚合物基复合材料的压倒性需求，所述轻质聚合物基复合材料具有满足新型节能“绿色”车辆标准的苛刻的机械、物理和功能特性。这项国际合作将通过理论和实验方法结合合作者的专业知识，促进先进工程复合材料的开发。本项目旨在实现以下目标：（1）开发各种基于机器学习的复合材料框架，根据设计空间和注塑工艺的大小，有效地设计和优化聚合物复合材料结构;（2）设计和制造具有上级性能的多功能轻质复合材料。在先进复合材料的设计和制造中，了解材料和工艺参数对所制造的复合材料的微观结构的影响是至关重要的，因为这些结构将直接决定这些材料的固有性能。机器学习技术的最新进展有可能加快与复合材料结构和性能相关的制造参数相关的设计和优化过程。这些技术是非常有利的，因为它们需要最少的实验数据来开发优化框架。实施这种方法将导致创建高性能轻质复合材料，可以取代传统材料，从而提高车辆的能源效率，同时降低其整体环境影响。