人工智能在复合材料研究中的应用

张峻铭; 杨伟东; 李岩

doi:10.6052/1000-0992-21-019

人工智能在复合材料研究中的应用

doi: 10.6052/1000-0992-21-019

同济大学航空航天与力学学院, 上海 200092

基金项目: 国家杰出青年科学基金(11625210)、国家重点研发计划(2020YFB0311500)、上海市浦江人才计划(2020PJD072)以及中央高校基本科研业务费专项资金资助项目.

详细信息

作者简介:
杨伟东, 同济大学特聘研究员, 博士生导师. 东方学者特聘教授、上海市海外高层次引进人才、浦江人才计划. 研究方向包括人工智能赋能复合材料设计与制造、智能结构与柔性器件力学等. 近年来主持并参与国家自然科学基金、科技部重点研发计划子课题、新加坡国家研究基金会等课题. 相关成果在PNAS等学术期刊上发表SCI论文20余篇. 在剑桥大学出版社出版英文著作1部, 受权智能复合材料相关国际发明专利1项(国际公开). 中国力学学会、中国复合材料学会、国际计算力学学会等学术组织会员

李岩, 同济大学特聘教授, 博士生导师. 国家杰出青年科学基金获得者, 享受国务院特殊津贴. 入选国家万人计划领军人才、科技部创新推进计划中青年科技创新领军人才, 上海市优秀学术带头人等人才计划项目; 作为第一完成人, 获上海市教学成果一等奖1项, 上海市自然科学二等奖1项; 获评“宝钢教育奖”特等奖提名奖, 中国复合材料学会青年科学家奖. 主要研究方向为高性能复合材料的制备及力学, 特别是对植物纤维增强复合材料的高性能化和多功能化开展了深入的科学研究. 发表SCI收录论文80余篇, SCI他引逾2000次; 出版专著《生物质树脂、纤维及生物复合材料》(国家出版基金资助)、《植物纤维增强复合材料》; 获邀参与了4 部关于植物纤维增强复合材料英文专著的编写工作; 担任《复合材料学报》《力学季刊》副主编, 《Composites Science and Technology》《Composites Part A》《Composites Part B》《Composites Communications》《Acta Mechanica Sinica》等期刊编委, 亚澳复合材料学会理事, SAMPE中国常务理事及绿色复合材料专业委员会主任, 中国复合材料学会常务理事, 中国力学学会理事, 上海市力学学会副理事长, 上海市航空学会常务理事, 上海市宇航学会常务理事等学术职务

通讯作者:
20501@tongji.edu.cn

中图分类号: TB33, O3
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出版历程
- 收稿日期: 2021-04-15
- 录用日期: 2021-07-20
- 网络出版日期: 2021-07-26
- 刊出日期: 2021-11-26

Application of artificial intelligence in composite materials

School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China

More Information

Corresponding author: 20501@tongji.edu.cn

摘要

摘要: 复合材料以其轻质高强高模、可设计性强等优点成为结构轻量化的重要用材. 然而, 随着复合材料组分、结构以及性能需求的日益复杂化, 以实验观测、理论建模和数值模拟为主体的传统研究范式, 在复合材料力学性能分析、设计和制造等方面遇到了新的科学问题与技术瓶颈. 其中, 实验观测不足、理论模型缺乏、数值分析受限、结果验证困难等问题在一定程度上制约了先进复合材料在面向未来工程领域中应用的发展. 人工智能方法以数据驱动的模型替代传统研究中的数学力学模型, 直接由高维高通量数据建立变量间的复杂关系, 捕捉传统力学研究方法难以发现的规律, 在复杂系统的分析、预测、优化方面拥有与生俱来的优势. 而通过人工智能赋能来寻求复合材料中传统研究方法所面临难题的新的解决方案, 目前已成为复合材料研究领域的发展趋势. 本文综述并评价了人工智能方法在复合材料性能预测、优化设计、制造检测及健康监测等方面的研究进展, 并对未来发展方向进行了探讨和展望.
- 复合材料 /
- 人工智能 /
- 力学性能 /
- 设计 /
- 制造
Abstract: Composite materials have become the major materials of light-weight structure due to their light weight, high strength, high modulus, and strong designability. However, as the component, structure and requirements of capability become increasingly complex, traditional research methods based on experiment, theoretical modeling and numerical simulation meet lots of new problems in the properties prediction, design optimization, manufacturing and processing of composite materials. Insufficient experimental observation, lacking theoretical model, constrained numerical simulation and difficult conclusion validation have seriously restricted the development of advanced composite materials in the future-oriented engineering. Instead of the mathematical models used by mechanics, data-driven models are used in the Artificial Intelligence. It directly establishes the complex relationship between variables from high-dimensional and high-throughput data, then captures the laws that are difficult to be discovered by traditional mechanical method, shows natural advantages in simulation, prediction, optimization in complex systems. It has become the development trend in the field of composite materials to find new solutions to the problems faced by traditional methods through Artificial Intelligence. In this paper, the status of properties prediction, design optimization, manufacturing and health monitoring is introduced. The future development direction of this field is discussed.
- composite materials /
- artificial intelligence /
- mechanical properties /
- design /
- manufacturing

HTML全文

图 1 复合材料领域的人工智能应用

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图 2 人工智能发展历程

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图 3 复合材料研究中人工智能应用步骤

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图 4 基于材料三维微结构的性能预测

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图 5 基于二维切片方法的材料微结构性能预测

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图 6 通过间接测量学习本构模型的神经网络系统

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图 7 实验、有限元分析和人工神经网络相结合的性能预测模式

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图 8 基于神经网络与智能算法的优化设计方法

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图 9 逆设计生成网络(Chen & Gu 2020)

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图 10 基于人工智能的自动铺丝视觉检查系统(Sacco et al. 2020)

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图 11 人工智能与声发射技术结合的复合材料加工状态监测

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图 12 人工智能与压电材料结合的分层损伤检测

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图 13 智能复合材料——以光纤智能复合材料为例

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