Please wait a minute...
工程设计学报
工程设计学报  2019, Vol. 26 Issue (6): 666-674    DOI: 10.3785/j.issn.1006-754X.2019.00.009
智能设计     
基于数字孪生与深度学习技术的制造加工设备智能化方法研究
王安邦, 孙文彬, 段国林
河北工业大学 机械工程学院， 天津 300401
Research on intelligent method of manufacturing and processing equipment based on digital twin and deep learning technology
WANG An-bang, SUN Wen-bin, DUAN Guo-lin
School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
 全文: PDF(3164 KB)   HTML
摘要： 针对传统制造加工设备在生产加工过程中存在设备与数据信息联系不紧密，设备使用维护多依赖于人工经验等问题，提出了一种新的设备智能化方法。首先，在信息层建立能反映制造加工设备真实状态的数字孪生体；其次，基于历史加工大数据，通过数字孪生体对加工过程的行为进行建模及深度学习和训练，并利用训练好的人工神经网络根据采集到的实时数据来预测制造加工设备下一时刻的状态，使制造加工设备实现物理层与信息层数据的深度融合，拥有自我感知、自我预测的能力，最终实现智能化；最后，以浆料微流挤出成型设备挤出结构系统的智能化实施过程为例，验证了所提出方法的可行性。实例结果表明该设备智能化方法可有效地对挤出结构系统的运行状态进行监测及预测，为后续提高挤出成型精度提供了有效的数据信息。研究表明数字孪生和深度学习技术能够提升制造加工设备的智能化程度，可为未来智能制造的发展提供理论支撑。
关键词： 制造加工设备;  智能化;  数字孪生;  深度学习    
Abstract: In view of the problems that the traditional manufacturing and processing equipment is not closely related to the data information during the production and the equipment maintenance greatly depends on the human experience, a new intelligent method for the equipment is proposed. Firstly, the digital twin that can reflect the true state of manufacturing and processing equipment was established in the information layer. Secondly, based on the past big data of processing，the behaviour of the process was modelled and the deep learning and training was performed by the digital twin. The state of manufacturing and processing equipment at the next moment was predicted by the trained artificial neural network based on the collected real-time data，so that the manufacturing and processing equipment could realize the deep integration of data in the physical layer and the information layer, and had the ability of self-awareness and self-prediction to realize the intelligence. Finally, taking the intelligent implementation process of extrusion structure system of slurry microfluidic extrusion forming equipment as an example to verify the feasibility of the proposed method. The example results showed that the intelligent method of the equipment could effectively monitor and predict the operating state of the extrusion structure system, which provides effective data information for the subsequent improvement of the extrusion molding accuracy. Research shows that the digital twin and deep learning technology can enhance the intelligence of manufacturing and processing equipment, and can provide theoretical support for the development of intelligent manufacturing in the future.
Key words: manufacturing and processing equipment    intelligence    digital twin    deep learning
收稿日期: 2019-06-11 出版日期: 2019-12-28
CLC:  TP 301.6  
基金资助: 河北省重点研发计划资助项目（17211808D）
通讯作者: 段国林（1963—），男，河北保定人，教授，博士生导师，博士，从事CAD/CAM、智能工程及自动化等研究，E-mail:glduan@hebut.edu.cn     E-mail: glduan@hebut.edu.cn
作者简介: 王安邦(1994—)，男，湖北孝感人，硕士生，从事数字孪生、增材制造研究，E-mail:wab0666@163.com，https://orcid.org/0000-0003-0872-1569
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
王安邦
孙文彬
段国林

引用本文:

王安邦, 孙文彬, 段国林. 基于数字孪生与深度学习技术的制造加工设备智能化方法研究[J]. 工程设计学报, 2019, 26(6): 666-674.

WANG An-bang, SUN Wen-bin, DUAN Guo-lin. Research on intelligent method of manufacturing and processing equipment based on digital twin and deep learning technology. Chinese Journal of Engineering Design, 2019, 26(6): 666-674.

链接本文:

https://www.zjujournals.com/gcsjxb/CN/10.3785/j.issn.1006-754X.2019.00.009    或    https://www.zjujournals.com/gcsjxb/CN/Y2019/V26/I6/666

[1] YURI K, DMITRII S. Artificial intelligence and cyber-physical mechanical processing systems in digital industry[J]. MATEC Web of Conferences, 2018, 2(3): 7-12. doi:10.1051/matecconf/201822605001
[2] 姚锡凡, 景轩, 周佳军, 等. 走向新一代可持续制造——包容性制造[J].计算机集成制造系统,2019, 25(10):2419-2432. doi:10.13196/j.cims.2019.10.002 YU Xi-fan, JING Xuan, ZHOU Jia-jun, et al. Towards next generation sustainable manufacturing—inclusive manufacturing[J]. Computer Integrated Manufacturing Systems, 2019, 25(10): 2419-2432.
[3] 陶飞, 程颖, 程江峰, 等. 数字孪生车间信息物理融合理论与技术[J].计算机集成制造系统,2017,23(8): 1603-1611. doi:10.13196/j.cims.2017.08. 001 TAO Fei, CHENG Ying, CHENG Jiang-feng, et al. Theories and technologies for cyber-physical fusion in digital twin shop-floor[J]. Computer Integrated Manufacturing Systems, 2017, 23(8): 1603-1611.
[4] 张荣,李伟平,莫同.深度学习研究综述[J].信息与控制,2018,47(4):385-397,410. doi:10.13976/j.cnki.xk.2018.8091 ZHANG Rong, LI Wei-ping, MO Tong. Review of deep learning[J]. Information and Control, 2018, 47(4): 385-397, 410.
[5] SCHROEDER G N, STEINMETZ C, PEREIRA C E, et al. Digital twin data modeling with automationML and a communication methodology for data exchange[J]. IFAC-PapersOnLine, 2016, 49(30): 12-17. doi:10.1016/j. ifacol.2016.11.115
[6] ZHUANG C, LIU J, XIONG H. Digital twin-based smart production management and control framework for the complex product assembly shop-floor[J]. The International Journal of Advanced Manufacturing Technology, 2018, 96(1/4): 1149-1163. doi:10.1007/s00170-018-1617-6
[7] VACHALEK J, BARTALSKY L, ROVNY O, et al. The digital twin of an industrial production line within the industry 4.0 concept[C]//2017 21st International Conference on Process Control(PC), Strbske Pleso, Jun. 6-9, 2017. doi:10.1109/PC.2017. 7976223
[8] RAZAVI-FAR R, HALLAJI E, FARAJZADEH-ZANJANI M, et al. Information fusion and semi-supervised deep learning scheme for diagnosing gear faults in induction machine systems[J]. IEEE Transactions on Industrial Electronics, 2019, 66(8): 6331-6342. doi:10.1109/TIE. 2018.2873546
[9] ZHENG Yu, YANG Sen, CHENG Huan-chong. An application framework of digital twin and its case study[J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(3): 1141-1153. doi:10. 1007/s12652-018-0911-3
[10] 田明明, 叶继华, 王仕民, 等. 一种复杂环境下多传感器数据融合方法[J].山东大学学报(工学版),2019,49(3):1-7. doi:10.6040/j.issn.1672-3961.0.2017.426 TIAN Ming-ming, YE Ji-hua, WANG Shi-min, et al. A method of multi-sensor data fusion under the complicated environment[J].Journal of Shandong University (Engineering Science), 2019, 49(3): 1-7.
[11] LIU C, VENGAYIL H, LU Y, et al. A cyber-physical machine tools platform using OPC UA and MTConnect[J]. Journal of Manufacturing Systems, 2019, 51: 61-74. doi:10.1016/j.jmsy.2019.04.006
[12] 陶飞, 刘蔚然, 张萌, 等. 数字孪生五维模型及十大领域应用[J].计算机集成制造系统,2019,25(1):1-18. doi:10.13196/j.cims.2019.01.001 TAO Fei, LIU Wei-ran, ZHANG Meng, et al. Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems, 2019, 25(1): 1-18.
[13] LUO W, HU T, ZHANG C, et al. Digital twin for CNC machine tool: modeling and using strategy[J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(3): 1129-1140. doi:10.1007/s12652-018-0946-5
[14] 刘恒利. 基于多物理域信息多模式融合与深度学习的智能加工机器自主感知方法研究[D].深圳:深圳大学机电与控制工程学院,2017：38-40. doi:CNKI:CDMD:2.1017.812416 LIU Heng-li. Research on autonomous sensing method of intelligent maching machine based on multi-mode fusion and deep learning of multi-physics information[D]. Shenzhen: Shenzhen University, School of Mechatronics and Control Engineering， 2017： 38-40.
[15] NIE Wei-zhi, WANG Kun, WANG Hong-tao, et al. The assessment of 3D model representation for retrieval with CNN-RNN networks[J]. Multimedia Tools and Applications, 2019, 78(12)： 16979-16994. doi: 10. 1007/s11042-018-7102-2
[16] 步亚男. 桌面级3D打印机系统的设计与实现[D]. 大连:辽宁师范大学计算机与信息技术学院, 2017：3-4. doi:CNKI:CDMD:2.1017.115941 BU Ya-nan. Design and implementation of desktop 3D printing systems [D]. Dalian: Liaoning Normal University, School of Computer and Information Technology， 2017： 3-4.
[17] 周婧, 段国林, 卢林, 等. 陶瓷浆料微流挤压成形关键问题研究[J].中国机械工程,2015,26(22):3097-3102. doi:10.3969/j.issn.1004-132X.2015.22.018 ZHOU Jing, DUAN Guo-lin, LU Lin, et al. Research on several key problems of microflow extrusion forming of ceramic slurry[J]. China Mechanical Engineering, 2015, 26(22): 3097-3102. doi:10.3969/j.issn.1004-132X.2015.22.018
[18] 闫存富. 陶瓷膏体低温成形过程控制及液相迁移研究[D].西安:西安理工大学机械与精密仪器工程学院,2018：57-60. YAN Cun-fu. Study on process control and liquid phase migration of freeze-form extrusion fabrication for ceramic paste[D]. Xi'an: Xi'an University of Technology， School of Mechanical and Precision Instrument Engineering, 2018： 57-60.
[19] 周梅. PID-模糊控制联用法在塑料加工工艺控制方面的研究进展[J].工程塑料及应用,2018,46(6):143-147. doi:10.3969/j.issn.1001-3539.2018.06.029 ZHOU Mei. Research progress on the control of plastic processing technology by PID-fuzzy control[J]. Engineering Plastics Application, 2018, 46(6): 143-147.
[1] 傅贵武, 王兴波, 田英. 基于五轴加工中心智能生产线的数字孪生应用研究[J]. 工程设计学报, 2021, 28(4): 426-432.
[2] 尚志武, 周士琦. 基于图像监测的微量移液技术的研究[J]. 工程设计学报, 2021, 28(4): 495-503.
[3] 白仲航, 孙意为, 许彤, 丁满. 基于设计任务的概念设计中产品数字孪生模型的构建[J]. 工程设计学报, 2020, 27(6): 681-689.
[4] 卢万杰, 付华, 赵洪瑞. 基于深度学习算法的矿用巡检机器人设备识别[J]. 工程设计学报, 2019, 26(5): 527-533.
[5] 简德. 主动测量信息的智能化处理与控制[J]. 工程设计学报, 2000, 7(4): 101-104.