锻压技术

旋转压合成形数字孪生系统构建及应用

英文标题：Construction and application on rotating and pressing digital twin system

作者：宁玲玲郑健刘冉冉

单位：山东工程职业技术大学

分类号：TP311

出版年,卷(期):页码：2023,48(11):115-123

摘要：

针对口罩生产这种新型旋转压合成形技术生产过程中的数字孪生技术进行了研究。研究阐述了数字孪生在旋转压合生产中的框架和系统组成，利用西门子的NXMCD软件构建了数字孪生模型，并建立了其中的运动关系，在运动关系上利用传感器和模型之间的映射来实现模型的驱动，实现了虚实同步。此外,通过接口将深度学习的神经网络和数字孪生系统结合，实现以虚预实，对传感器的数据源进行不断地迭代，实现了产线的实时优化。这种融合了数字孪生与旋转压合技术的方法，为制造业的智能制造提供了宝贵的数据和参考，推动了生产过程的高效化和智能化。

The digital twin technology in the production process of new rotating and pressing technology for mask production was studied, and the framework and system composition of digital twin in rotating and pressing production were researched and elaborated. Then, the digital twin model was constructed by Siemens software NXMCD, and the motion relationship was established. Furthermore, in the motion relationship, the mapping between sensor and model was used to drive the model and achieve virtual and real synchronization, and combining the deep learning neural network with the digital twin system was realized by the use of interface to realize the virtual prediction actual, continuous iteration of sensor data sources and real-time optimization of the production line. The result shows that this method combining the digital twin with the rotating and pressing technology provides valuable data and references for the intelligent manufacturing in this industry, and promotes the high efficiency and intelligence of the production process.

基金项目：

山东省教育厅公示的第二批教育部名师工作室建设项目（鲁教师函[2019]42号）；教育部产学合作协同育人项目（221000821124531）；2022年度山东省职业教育教学改革研究项目（2022065）

作者简介：宁玲玲（1980-），女，硕士，教授，E-mail：67155842@qq.com；通信作者：郑健（1984-），男，硕士，教授，E-mail：448383188@qq.com

参考文献：

[1]Adanur S, Jayswal A.Filtration mechanisms and manufacturing methods of face masks:An overview[J].Journal of Industrial Textiles，2022，51（S3）:3683S-3717S.

[2]沈刚,赵浩松,郭峰,等.基于Houdini的VEX程序化建模高效搭建数字孪生虚拟工厂[J].智能制造,2021,(4):91-96,101.

Shen G，Zhao H S，Guo F，et al. Houdini-based VEX procedural modeling for efficiently building digital twin virtual factories[J].Intelligent Manufacturing, 2021,(4):91-96,101.

[3]GB/T 40373—2021,一次性口罩制造包装生产线通用技术要求[S].

GB/T 40373—2021, Single-use face mask manufacturing and packaging production line—General technical requirements[S].

[4]孟松鹤,叶雨玫,杨强,等.数字孪生及其在航空航天中的应用[J].航空学报,2020,41(9):1-12.

Meng S H, Ye Y M, Yang Q, et al. Digital twin and its aerospace applications[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(9): 1-12.

[5]Tuegel E J, Ingraffea A R, Eason T G, et al.Reengineering aircraft structural life prediction using a digital twin[J].International Journal of Aerospace Engineering,2011,2011:1-14.

[6]Grieves M. Digital twin: Manufacturing excellence through virtual factory replication[J]. White Paper, 2014, 1(2014): 1-7.

[7]Zhu Z X, Xi X L, Xu X, et al. Digital twin-driven machining process for thin-walled part manufacturing[J]. Journal of Manufacturing Systems,2021,59:453-466.

[8]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.

Tao F, Liu W R, Zhang M, et al. Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems, 2019, 25(1): 1-18.

[9]陶飞,张辰源,戚庆林,等.数字孪生成熟度模型[J].计算机集成制造系统,2022,28(5):1267-1281.

Tao F, Zhang C Y, Qi Q L, et al. Digital twin maturity model[J]. Computer Integrated Manufacturing Systems, 2022, 28(5): 1267-1281.

[10]陆剑峰,夏路遥,张浩,等.制造企业数字孪生生态系统的研究与应用[J].计算机集成制造系统, 2022,28(8):2273-2290.

Lu J F, Xia L Y, Zhang H, et al. Research and application of manufacturing enterprises digital twin ecosystem[J]. Computer Integrated Manufacturing Systems, 2022,28(8):2273-2290.

[11]Sprovieri J. Automation boosts production of masks[J]. Assembly,2021,64(10)：21.

[12]蔡文站,田建艳,王书宇,等.基于NXMCD与TIA的机器人打磨联合虚拟调试研究[J].现代制造工程,2022，(7):37-42，120.

Cai W Z, Tian J Y, Wang S Y, et al. Research of joint virtual commissioning of robotic grinding based on NX MCD and TIA[J]. Modern Manufacturing Engineering, 2022, (7): 37-42, 120.

[13]侯星宇,赵飞,王骏.基于MCD-TIA的换刀装置机电虚拟调试[J].煤矿机械,2022,43(6):75-77.

Hou X Y, Zhao F, Wang J. Electromechanical virtual commissioning of tool changer based on MCD-TIA[J]. Coal Mine Machinery, 2022, 43(6): 75-77.

[14]赵林,吴双,张可义,等.基于NXMCD的堆垛机机电概念设计[J].制造业自动化,2021,43(11):114-116.

Zhao L, Wu S, Zhang K Y, et al. NX MCD-based mechatronic concept design for stacker cranes[J]. Manufacturing Automation, 2021, 43(11): 114-116.

[15]代小龙,杨丹.基于NXMCD的冲压生产线运动仿真研究[J].模具工业,2021,47(10):8-11,33.

Dai X L, Yang D. Research on motion simulation of stamping production line based on NXMCD[J]. Die & Mould Industry, 2021, 47(10): 8-11, 33.

[16]彭宇升,孙勇,凌云汉.航空锻造单元数字孪生系统构建及应用[J].锻压技术,2022,47(4):51-61.

Peng Y S, Sun Y, Ling Y H. Construction and application of digital twin system for aviation forging cell[J]. Forging & Stamping Technology, 2022, 47(4): 51-61.

[17]陈江明,贾锐,段辉.BP神经网络在涡轴发动机参数换算中的应用[J].制造业自动化,2022,44(7):31-35.

Chen J M, Jia R, Duan H. Application of BP neural networkin performance parameter correction of turboshaft engine[J]. Manufacturing Automation, 2022, 44(7): 31-35.

[18]胡浩帆.利用BP神经网络进行柴油机磨损故障监测[J].广东造船,2022,41(3):82-85.

Hu H F. Wear degree diagnosis of diesel engine parts[J]. Guangdong Shipbuilding, 2022, 41(3): 82-85.

[19]吴雁，王晓军，何勇，等.数字孪生在制造业中的关键技术及应用研究综述[J].现代制造工程，2021，（9）：137-145.

Wu Y, Wang X J, He Y, et al. Review on the technology and application of digital twin in manufacturing industry[J]. Modern Manufacturing Engineering, 2021，(9): 137-145.

[20]李浩，王昊琪，刘根，等.工业数字孪生系统的概念、系统结构与运行模式[J].计算机集成制造系统，2021，27（12）：3373-3390.

Li H, Wang H Q, Liu G, et al. Concept,system structure and operating mode of industrial digital twin system[J]. Computer Integrated Manufacturing Systems, 2021, 27(12): 3373-3390.

[21]陶飞，张萌，程江峰，等.数字孪生车间——一种未来车间运行新模式[J].计算机集成制造系统，2017，23（1）：1-9.

Tao F, Zhang M, Cheng J F, et al. Digital twin workshop:A new paradigm for future workshop[J]. Computer Integrated Manufacturing Systems, 2017, 23(1): 1-9.

[22]张淑华，王文权.基于BP神经网络的前轴锻造工艺优化[J].热加工工艺，2020，49(19)：115-117.

Zhang S H, Wang W Q. Forging process optimization of front shaft based on BP neural network[J]. Hot Working Technology, 2020, 49(19): 115-117.

[23]张亚敏，姜永亮.基于神经网络算法的铝基复合材料搅拌铸造工艺优化[J].热加工工艺，2021，50(18)：91-94.

Zhang Y M, Jiang Y L. Optimization of stirring casting process for aluminum matrix composites based on neural network algorithm[J]. Hot Working Technology, 2021, 50(18): 91-94.

[24]王彦飞，朱悉铭，张明志，等.基于前馈神经网络的等离子体光谱诊断方法[J].物理学报，2021，70(9):155-166.

Wang Y F, Zhu X M, Zhang M Z, et al. Plasma optical emission spectroscopy based on feedforward neural network[J]. Acta Physica Sinica, 2021, 70(9): 155-166.

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