知识驱动的加工产品数字孪生拟态建模方法

doi:10.3901/JME.2021.23.182

机械工程学报 ›› 2021, Vol. 57 ›› Issue (23): 182-194.doi: 10.3901/JME.2021.23.182

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知识驱动的加工产品数字孪生拟态建模方法

刘世民¹, 孙学民¹, 陆玉前², 王柏村^3,4, 鲍劲松¹, 郭国强⁴

1. 东华大学机械工程学院上海 201600;
2. 奥克兰大学机械工程系奥克兰 1142 新西兰;
3. 浙江大学机械工程学院杭州 310027;
4. 上海航天精密机械研究所上海 201600

收稿日期:2020-12-20 修回日期:2021-08-11 出版日期:2021-12-05 发布日期:2022-02-28
通讯作者: 鲍劲松(通信作者),男,1972年出生,教授,博士研究生导师。主要研究方向为智能制造、工业智能等。E-mail:bao@dhu.edu.cn
作者简介:刘世民,男,1994年出生,博士研究生。主要研究方向为数字化制造、数字孪生技术。E-mail:1182021@mail.dhu.edu.cn;孙学民,男,1991年出生,博士研究生。主要研究方向为数字化装配、数字孪生技术。E-mail:312164355@qq.com;陆玉前,男,1990年出生,讲师。主要研究方向为人工智能,智能制造系统。E-mail:yuqian.lu@auckland.ac.nz;王柏村,男,1990年出生,百人计划研究员。主要研究方向为智能制造、人-信息-物理系统(HCPS)、能源系统优化等。E-mail:baicunw@umich.edu;郭国强,男,1982年出生,高级工程师。主要研究方向为难加工材料高速精密加工技术,高效加工专家知识库等。E-mail:guoguo_0@163.com
基金资助:
中国国家重点研发计划（2019YFB1706300）、国家科技重大专项（2018ZX04011001）和中央高校基本科研业务费专项资金（CUSF-DH-D-2020056）资助项目。

A Knowledge-Driven Digital Twin Modeling Method for Machining Products Based on Biomimicry

LIU Shimin¹, SUN Xuemin¹, LU Yuqian², WANG Baicun^3,4, BAO Jinsong¹, GUO Guoqiang⁴

1. School of Mechanical Engineering, Dong Hua University, Shanghai 201600;
2. Department of Mechanical Engineering, The University of Auckland, Auckland 1142, New Zealand;
3. School of Mechanical Engineering, Zhejiang University, Hangzhou 310027;
4. Shanghai Research Institute of Precise Aerospace Machinery, Shanghai 201600

Received:2020-12-20 Revised:2021-08-11 Online:2021-12-05 Published:2022-02-28

摘要/Abstract

摘要： 对加工过程的实时观察、分析和控制是优化零部件加工策略的重要组成部分。通过融合几何状态、物理状态和设备状态变化等多维、实时的加工过程数据，可以实现对加工过程的建模和监控。数字孪生模型是数字孪生系统的核心与基础。但是，目前还缺乏一种系统且能自适应开发高保真、多尺度、多维加工数字孪生建模方法，以辅助系统进行分析与决策。提出了一种知识驱动的加工产品数字孪生拟态建模方法，可以在加工过程中自适应地构建产品数字孪生模型。该模型可根据加工过程自适应的变化，实时表达加工过程中的产品，为数字孪生决策系统提供数据支持。最后在一个航天零件的加工过程中测试了该方法，验证数字孪生拟态模型在加工中应用的可行性。

关键词: 数字孪生, 生物拟态, 模型演化, 信息建模

Abstract: Real-time observation, analysis, and control of the machining process are the critical parts of optimizing the machining strategy of parts. By fusing multi-dimensional and real-time processing data such as geometry, physical state and equipment state, the modeling and monitoring of the machining process can be realized. The digital twin model is the core and foundation of the digital twin system. However, there is still a lack of a systematic and adaptive development modeling method of high-fidelity, multi-scale, multi-dimensional digital twin model. A knowledge-driven digital twin mimic modeling method for machining products is proposed, which can adaptively construct the digital twin model during the machining process. According to the adaptive evolution of the machining process, the model can express the products in the process in real-time and provide data support for the digital twin decision-making system. Finally, the method is tested in a case on an aerospace part to verify the feasibility of applying the digital twin mimic model in machining.

Key words: digital twin, biomimicry, model evolution, information model

中图分类号:

TG391

刘世民, 孙学民, 陆玉前, 王柏村, 鲍劲松, 郭国强. 知识驱动的加工产品数字孪生拟态建模方法[J]. 机械工程学报, 2021, 57(23): 182-194.

LIU Shimin, SUN Xuemin, LU Yuqian, WANG Baicun, BAO Jinsong, GUO Guoqiang. A Knowledge-Driven Digital Twin Modeling Method for Machining Products Based on Biomimicry[J]. Journal of Mechanical Engineering, 2021, 57(23): 182-194.

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知识驱动的加工产品数字孪生拟态建模方法

A Knowledge-Driven Digital Twin Modeling Method for Machining Products Based on Biomimicry

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