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数字孪生在制造中的应用进展综述

郭亮 张煜

郭亮, 张煜. 数字孪生在制造中的应用进展综述[J]. 机械科学与技术, 2020, 39(4): 590-598. doi: 10.13433/j.cnki.1003-8728.20190156
引用本文: 郭亮, 张煜. 数字孪生在制造中的应用进展综述[J]. 机械科学与技术, 2020, 39(4): 590-598. doi: 10.13433/j.cnki.1003-8728.20190156
Guo Liang, Zhang Yu. Review on Application Progress of Digital Twin in Manufacturing[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(4): 590-598. doi: 10.13433/j.cnki.1003-8728.20190156
Citation: Guo Liang, Zhang Yu. Review on Application Progress of Digital Twin in Manufacturing[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(4): 590-598. doi: 10.13433/j.cnki.1003-8728.20190156

数字孪生在制造中的应用进展综述

doi: 10.13433/j.cnki.1003-8728.20190156
基金项目: 

国家留学基金委员会 201708515160

国家自然科学基金项目 51705438

详细信息
    作者简介:

    郭亮(1985-), 副教授, 博士, 研究方向为云制造, 智能制造, gl@swpu.edu.cn

  • 中图分类号: TP391.9

Review on Application Progress of Digital Twin in Manufacturing

  • 摘要: 随着工业4.0、CPS、智能制造等研究的深入,如何解决制造中物理世界与信息世界之间的交互共融成为进一步推进制造业变革的核心问题。在此背景下,学术界和工业界提出了数字孪生的概念及技术体系,用于解决上述难题。为了全面了解数字孪生研究进展,首先梳理了数字孪生的基本概念,综述了其在航空航天、产品、制造设备及制造车间等阶段的应用进展,重点分析了数字孪生与物联网、大数据、CPS之间的联系与区别,最后指出了数字孪生在制造领域的发展趋势。
  • 图  1  数字孪生的构成[5]

    图  2  2013~2018年数字孪生文献数量

    图  3  数字孪生与物联网的内在联系

    图  4  产品数字孪生需要收集、处理与分析的数据

    图  5  CPS的5C体系结构与数字孪生

    图  6  数字孪生与物联网、大数据和CPS系统的相互关系

    表  1  学术/工业界对数字孪生的定义

    机构/作者年份定义
    美国空军研究实验室和NASA[10]2011一种面向飞行器或系统的高集成度多物理场、多尺度、多概率的仿真模型, 能够利用物理模型、传感器数据和历史数据等反映与该模型对应实体的功能、实时状态及演变趋势。
    Edward Glaessgen
    David Stargel[11]
    2012数字孪生是一个综合多物理、多尺度、多概率模拟的复杂系统, 使用最佳的物理模型, 传感器更新, 飞行器历史等, 镜像其相应飞行器数字孪生的生命。
    Michael Grieves
    John Vickers[5]
    2017数字孪生是从微观原子级到宏观几何级全面描述潜在生产或实际制造产品的虚拟信息结构。构建数字孪生的最佳结果是, 任何可以通过检测实际制造产品所获得的信息, 都可以从它的数字孪生中获得。
    庄存波等[6]2017产品数字孪生体是指物理实体的工作状态和工作进展在信息空间的全要素重建及数字化映射, 是一个集成多物理、多尺度、超写实、动态概率的仿真模型, 可用来模拟、监控、诊断、预测、控制产品物理实体在现实环境中的生产过程、状态和行为。
    陶飞等[12]2018数字孪生是产品全生命周期(PLM)的一个组成部分, 利用产品生命周期中的物理数据、虚拟数据和交互数据对产品进行实时映射。
    Haag Sebastian
    Anderl Reiner[13]
    2018数字孪生是单个产品的全面数字化表示, 它通过模型和数据包括实际生命对象的属性、条件以及行为, 数字孪生是一组可以模拟它在已部署环境中实际行为的现实模型。
    下载: 导出CSV

    表  2  国内外数字孪生研究情况

    应用对象年份研究内容目标
    飞行器2011~
    至今
    1.利用超高保真模型, 对飞行过程中的局部损伤
    和组织变化进行探测;
    2.结合数字孪生模型对飞行器进行实时监测;
    3.利用数字孪生模型对飞行器健康状况进行
    评估。
    1.减少结构件“意外”失效;
    2.飞行器损伤(疲劳裂纹、复合材料蠕变等)
    预测;
    3.飞行器寿命预测;
    4.飞行器状态管理。
    产品2015~
    至今
    1.利用数字孪生模型进行产品的个性化生产;
    2.将产品数字孪生模型融入到产品设计与生产
    过程。
    1.实现产品快速设计, 提高生产效率;
    2.实现个性化产品定制;
    3.实现模块化设计和高度可伸缩性生产。
    制造设备2016~
    至今
    1.对3D打印机建立数字孪生模型;
    2.对数控机床建立数字孪生模型;
    3.对自动导引运输车(AGV)建立数字孪生模型。
    1.减少实验次数和生产缺陷;
    2.对机器故障进行诊断和预测;
    3.实现自动控制、参数可视化以及实时状态监控;
    4.准确定位与自动路径规划。
    制造过程/制造车间2016~
    至今
    1.探索产品数字孪生模型在制造过程中的应用;
    2.探索制造车间虚拟化的机制与实现方法;
    3.探索数字孪生在中小制造企业中的应用。
    1.自动规划产品生产、装配过程, 优化生产资源;
    2.提高车间制造设备工作效率, 优化生产过程;
    3.利用学习工厂实现制造业向智能制造升级。
    下载: 导出CSV
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  • 收稿日期:  2019-04-01
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