Digital twin and blockchain enhanced smart manufacturing service collaboration and management

Abstract Recently, as the development of information technologies and personalized needs, the Industrial Internet platform based manufacturing service collaboration becomes the main method for manufacturing collaboration, where multiple interest-independent stakeholders involve. However, the distrust between stakeholders and the platform (distrust among collaborators and the doubts of data accuracy) hinders the development of it. Manufacturing service that are mainly encapsulated by static information cannot well adapt to changing physical conditions, which will disappoint the stakeholders for data accuracy. In addition, distrust among unfamiliar stakeholders is also the reason to keep users away from the platform. Digital twin (DT) and blockchain (BC) technologies have been widely studied in many fields, as they can provide a trustful method for the process of cyber-physical integration. In view of the insufficient interaction of physical and cyber spaces and distrust challenges encountered in the development of the current Industrial Internet platform based manufacturing service collaboration, this paper proposes a DT-BC enhanced manufacturing service collaboration mechanism towards the Industrial Internet platform. In addition, based on the analysis of manufacturing collaboration development, the DT-BC enhanced manufacturing service management, challenges, and future work of implementing DT-BC enhanced manufacturing service management for the Industrial Internet Platform are discussed.

[1]  Ting Qu,et al.  RFID-enabled gateway product service system for collaborative manufacturing alliances , 2011 .

[2]  Lida Xu,et al.  Compressed Sensing Signal and Data Acquisition in Wireless Sensor Networks and Internet of Things , 2013, IEEE Transactions on Industrial Informatics.

[3]  Fei Tao,et al.  Long/Short-Term Utility Aware Optimal Selection of Manufacturing Service Composition Toward Industrial Internet Platforms , 2019, IEEE Transactions on Industrial Informatics.

[4]  Pei Li,et al.  Manufacturing services collaboration: connotation, framework, key technologies, and research issues , 2020 .

[5]  Xudong Chai,et al.  INDICS: An Industrial Internet Platform , 2018, 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).

[6]  Shuai Zhang,et al.  Correlation-aware manufacturing service composition model using an extended flower pollination algorithm , 2018, Int. J. Prod. Res..

[7]  Dechen Zhan,et al.  Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved Artificial Bee Colony optimisation algorithm , 2015 .

[8]  Fei Tao,et al.  Application and modeling of resource service trust-QoS evaluation in manufacturing grid system , 2009 .

[9]  Ashley A. Bush,et al.  Platform Evolution: Coevolution of Platform Architecture, Governance, and Environmental Dynamics , 2010 .

[10]  Junliang Wang,et al.  A collaborative architecture of the industrial internet platform for manufacturing systems , 2020, Robotics Comput. Integr. Manuf..

[11]  Harris Wu,et al.  A flexible QoS-aware Web service composition method by multi-objective optimization in cloud manufacturing , 2016, Comput. Ind. Eng..

[12]  Joseph Sarkis,et al.  Blockchain technology and its relationships to sustainable supply chain management , 2018, Int. J. Prod. Res..

[13]  Fei Tao,et al.  A manufacturing services collaboration framework toward industrial internet platforms , 2018, 2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR).

[14]  Feng Qian,et al.  An agent-based service-oriented integration architecture for chemical process automation , 2015 .

[15]  Felipe Núñez,et al.  Neural Network-Based Model Predictive Control of a Paste Thickener Over an Industrial Internet Platform , 2020, IEEE Transactions on Industrial Informatics.

[16]  Zhiwen Zhang,et al.  Collaboration of large equipment complete service under cloud manufacturing mode , 2014 .

[17]  Anne L'Anton,et al.  A modeling framework for manufacturing services in Service-oriented Holonic Manufacturing Systems , 2016, Eng. Appl. Artif. Intell..

[18]  Duc Truong Pham,et al.  Perception data-driven optimization of manufacturing equipment service scheduling in sustainable manufacturing , 2016 .

[19]  Jose L. Martinez Lastra,et al.  A Petri net-based approach to incremental modelling of flow and resources in service-oriented manufacturing systems , 2012 .

[20]  Pingyu Jiang,et al.  Makerchain: A blockchain with chemical signature for self-organizing process in social manufacturing , 2019, Journal of Cleaner Production.

[21]  Fei Tao,et al.  Blockchain-Based Trust Mechanism for IoT-Based Smart Manufacturing System , 2019, IEEE Transactions on Computational Social Systems.

[22]  Meng Zhang,et al.  Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing , 2017, IEEE Access.

[23]  Lihui Wang,et al.  Current status and advancement of cyber-physical systems in manufacturing , 2015 .

[24]  Yaqin Wu,et al.  Hybrid Consensus Algorithm Optimization: A Mathematical Method Based on POS and PBFT and Its Application in Blockchain , 2020, Mathematical Problems in Engineering.

[25]  Xun Xu,et al.  A semantic web-based framework for service composition in a cloud manufacturing environment , 2017 .

[26]  Wenyu Zhang,et al.  A time-aware Bayesian approach for optimal manufacturing service recommendation in distributed manufacturing environments , 2013 .

[27]  Yefa Hu,et al.  QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system , 2014, Central Eur. J. Oper. Res..

[28]  Di Hu,et al.  Research on services encapsulation and virtualization access model of machine for cloud manufacturing , 2015, Journal of Intelligent Manufacturing.

[29]  Ray Y. Zhong,et al.  An augmented Lagrangian coordination method for optimal allocation of cloud manufacturing services , 2017, Journal of Manufacturing Systems.

[30]  Fei Hao,et al.  An on-demand coverage based self-deployment algorithm for big data perception in mobile sensing networks , 2018, Future Gener. Comput. Syst..

[31]  He Zhang,et al.  Digital Twin in Industry: State-of-the-Art , 2019, IEEE Transactions on Industrial Informatics.

[32]  Pingyu Jiang,et al.  Modeling and analyzing of an enterprise collaboration network supported by service-oriented manufacturing , 2012 .

[33]  Fei Tao,et al.  Consensus aware manufacturing service collaboration optimization under blockchain based Industrial Internet platform , 2019, Comput. Ind. Eng..

[34]  Naveen Verma,et al.  Exploiting Emerging Sensing Technologies Toward Structure in Data for Enhancing Perception in Human-Centric Applications , 2019, IEEE Internet of Things Journal.

[35]  Andrew Y. C. Nee,et al.  A Cooperative Co-Evolutionary Algorithm for Large-Scale Process Planning With Energy Consideration , 2017 .

[36]  Naixue Xiong,et al.  A Kernel-Based Compressive Sensing Approach for Mobile Data Gathering in Wireless Sensor Network Systems , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[37]  Meng Zhang,et al.  Digital Twin Enhanced Dynamic Job-Shop Scheduling , 2020 .

[38]  Fei Tao,et al.  Logistics-aware manufacturing service collaboration optimisation towards industrial internet platform , 2018, Int. J. Prod. Res..

[39]  Haibo Li,et al.  Composition of Resource-Service Chain for Cloud Manufacturing , 2016, IEEE Transactions on Industrial Informatics.

[40]  Fei Tao,et al.  Utility modelling, equilibrium, and coordination of resource service transaction in service-oriented manufacturing system , 2012 .

[41]  Fei Tao,et al.  Study of failure detection and recovery in manufacturing grid resource service scheduling , 2010 .

[42]  Tom Van Woensel,et al.  Cloud manufacturing service selection optimization and scheduling with transportation considerations: mixed-integer programming models , 2018 .

[43]  Fei Tao,et al.  DT-II: Digital twin enhanced Industrial Internet reference framework towards smart manufacturing , 2020, Robotics Comput. Integr. Manuf..

[44]  Xifan Yao,et al.  A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition , 2017 .

[45]  Ray Y. Zhong,et al.  Extending augmented Lagrangian coordination for the optimal configuration of cloud-based smart manufacturing services with production capacity constraint , 2019, Robotics Comput. Integr. Manuf..

[46]  Keith A. Teague,et al.  Collaborative and Compressed Mobile Sensing for Data Collection in Distributed Robotic Networks , 2018, IEEE Transactions on Control of Network Systems.

[47]  Ray Y. Zhong,et al.  A RFID-enabled positioning system in automated guided vehicle for smart factories , 2017 .

[48]  Liang Guo,et al.  Study on machining service modes and resource selection strategies in cloud manufacturing , 2015 .

[49]  Yong Chen,et al.  Combining social network and collaborative filtering for personalised manufacturing service recommendation , 2013 .

[50]  Hongming Cai,et al.  Linked Semantic Model for Information Resource Service Toward Cloud Manufacturing , 2017, IEEE Transactions on Industrial Informatics.