Generalized distributed four-domain digital twin system for intelligent manufacturing

Abstract

Discrete manufacturing workshops are confronted with problems of processing diverse products and strict realtime requirements for data service calculation and manufacturing equipment, which makes it difficult to provide realtime feedback and compensation. In this study, a high-availability, high-performance, and high-concurrency digital twin reference model was constructed to satisfy a large number of manufacturing requirements. A multiterminal real-time interaction model and information aging classification rules for virtual and physical models were established. Moreover, a multiterminal virtual interaction model was proposed, and a generalized distributed computing service digital twinning system was developed. This digital twin system was considered a machine tool box processing line as an actual case. Consequently, a full closed-loop manufacturing process digital twin platform for physical request service, real-time response, and quality information feedback from iterations, which provides case guidance for subsequent factory digital twin systems, was realized. The proposed system can satisfy the requirements of multidevice big data computing services in modern manufacturing plants, as well as multiplatform, low-latency, and high-fidelity information visualization requirements for managers. Thus, this system is expected to play an important role in information factories.

Keywords

digital twin / distributed system / multiterminal interaction / container choreography system

References

[1] DingK, JiangP-Y. Incorporating social sensors, cyber-physical system nodes, and smart products for personalized production in a social manufacturing environment [J]. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 2018, 232(13): 2323-2338

[2] DingK, ChanF T S, ZhangX-D, et al.. Defining a digital twin-based cyber-physical production system for autonomous manufacturing in smart shop floors [J]. International Journal of Production Research, 2019, 57(20): 6315-6334

[3] ENGELSBERGER M, GREINER T. Software architecture for cyber-physical control systems with flexible application of the software-as-a-service and on-premises model [C]// 2015 IEEE International Conference on Industrial Technology (ICIT), 2015: 1544–1549. DOI: https://doi.org/10.1109/ICIT.2015.7125316.

[4] LiuX F, ShahriarM R, AlS S M N, et al.. Cyber-physical manufacturing cloud: Architecture, virtualization, communication, and testbed [J]. Journal of Manufacturing Systems, 2017, 43: 352-364

[5] LeeJ, AzamfarM, SinghJ, et al.. Integration of digital twin and deep learning in cyber-physical systems: Towards smart manufacturing [J]. IET Collaborative Intelligent Manufacturing, 2020, 2(1): 34-36

[6] KimW, SungM. Standalone OPC UA wrapper for industrial monitoring and control systems [J]. IEEE Access, 2018, 6: 36557-36570

[7] TaoF, ChengY, ChengJ-F, et al.. Theories and technologies for cyber-physical fusion in digital twin shop-floor [J]. Computer Integrated Manufacturing Systems, 2017, 23(8): 1603-1611(in Chinese)

[8] WangS-Y, CaiM-N, WuQ-X, et al.. DCRRDT: A method for deployment and control of RFID sensors under digital twin-driven for indoor supervision [M]. Algorithms and Architectures for Parallel Processing, 2020, Cham, Springer International Publishing, 567576

[9] MorganJ, O’DonnellG E. Cyber physical process monitoring systems [J]. Journal of Intelligent Manufacturing, 2018, 29(6): 1317-1328

[10] LiuZ-F, ChenW, ZhangC-X, et al.. Data super-network fault prediction model and maintenance strategy for mechanical product based on digital twin [J]. IEEE Access, 2019, 7: 177284-177296

[11] TaoF, QiQ-L, WangL-H, et al.. Digital twins and cyber-physical systems toward smart manufacturing and industry 4.0: Correlation and comparison [J]. Engineering, 2019, 5(4): 653-661

[12] LengJ-W, ZhangH, YanD-X, et al.. Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop [J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(3): 1155-1166

[13] RedelinghuysA J H, BassonA H, KrugerK. A six-layer architecture for the digital twin: A manufacturing case study implementation [J]. Journal of Intelligent Manufacturing, 2020, 31(6): 1383-1402

[14] WangX V, WangL-H. A cloud-based production system for information and service integration: An internet of things case study on waste electronics [J]. Enterprise Information Systems, 2017, 11(7): 952-968

[15] INNERBICHLER J, GONUL S, DAMJANOVIC-BEHRENDT V, et al. NIMBLE collaborative platform: Microservice architectural approach to federated IoT [C]// 2017 Global Internet of Things Summit (GIoTS), 2017: 1–6. DOI: https://doi.org/10.1109/GIOTS.2017.8016216.

[16] TaibiD, LenarduzziV, PahlC. Processes, motivations, and issues for migrating to microservices architectures: An empirical investigation [J]. IEEE Cloud Computing, 2017, 4(5): 22-32

[17] SteindlG, KastnerW. Semantic microservice framework for digital twins [J]. Applied Sciences, 2021, 11(12): 5633

[18] LiH, TaoF, WangH-Q, et al.. Integration framework and key technologies of complex product design-manufacturing based on digital twin [J]. Computer Integrated Manufacturing Systems, 2019, 2561320-1336

[19] SzallerA, EgriP, KadarB. Trust-based resource sharing mechanism in distributed manufacturing [J]. International Journal of Computer Integrated Manufacturing, 2020, 33(1): 1-21

[20] CiavottaM, DalM G, RovereD, et al.Towards the digital factory: A microservice-based middleware for real-to-digital synchronization [M], 2020, Cham, Springer

[21] AlamK M, ElS A. C2PS: A digital twin architecture reference model for the cloud-based cyber-physical systems [J]. IEEE Access, 2017, 5: 2050-2062

[22] LiuZ-F, YanJ, ChengQ, et al.. The mixed production mode considering continuous and intermittent processing for an energy-efficient hybrid flow shop scheduling [J]. Journal of Cleaner Production, 2020, 246119071