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

基本信息

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

项目摘要

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，环境意识的全球提高，导致对基于轻量的聚合物的压倒性复合材料的需求，这些复合材料具有苛刻的机械，物理和功能特征，符合新型能源效率“绿色”车辆的标准。这种国际合作将通过理论和实验方法结合合作者的专业知识来促进高级工程复合材料的开发。该项目旨在实现以下目标：（1）根据设计空间和注入成型过程的尺寸，开发各种基于机器的复合框架，以有效设计和优化聚合物复合结构，以及（2）工程师和制造具有出色表现的多功能轻量级复合材料。在高级复合材料的设计和制造中，重要的是要了解材料和加工参数对制造的复合材料的微观结构的影响，因为这些结构将直接确定这些材料的固有特性。机器学习技术的最新进步有可能加快与制造参数相关的设计和优化过程，从而有助于复合材料的结构和特性。这些技术非常有利，因为它们需要最小的实验数据才能开发优化框架。实施这种方法将导致创建高性能轻质复合材料，这些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。