An approach to develop a digital twin for industry 4.0 systems: manufacturing automation case studies

ABSTRACT The new paradigm of digital manufacturing and the concept of Industry 4.0 has led to the integration of recent manufacturing advances with modern information and communication technologies. Therefore, digital simulation tools fused into production systems can improve time and cost-effectiveness and enable faster, more flexible, and more efficient processes to produce higher-quality goods. The advancement of digital simulation with sensory data may support the credibility of production systems and improve the efficiency of production planning and execution processes. In this paper, an approach is proposed to develop a Digital Twin of production systems in order to optimize the planning and commissioning process. The proposed virtual cell interacts with the physical system with the help of different Digital Manufacturing Tools (DMT), which allows for the testing of various programs in a different scenario to check for any shortcomings before it is implemented on the physical system. Case studies from the different production systems are demonstrated to realize the feasibility of the proposed approach.

[1]  Rainer Stark,et al.  Development and operation of Digital Twins for technical systems and services , 2019, CIRP Annals.

[2]  Dimitris Mourtzis,et al.  Simulation in Manufacturing: Review and Challenges , 2014 .

[3]  S. Naveen,et al.  Globally accessible machine automation using Raspberry pi based on Internet of Things , 2015, 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[4]  Gunnar Bolmsjö,et al.  Remote control of a standard ABB robot system in real time using the Robot Application Protocol (RAP) , 2002 .

[5]  Mikell P. Groover,et al.  Automation, Production Systems, and Computer-Integrated Manufacturing , 1987 .

[6]  Kashif Mahmood,et al.  Performance Analysis of a Flexible Manufacturing System (FMS) , 2017 .

[7]  Sotiris Makris,et al.  A cyber-physical context-aware system for coordinating human-robot collaboration , 2018 .

[8]  Michael W. Grieves,et al.  Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems , 2017 .

[9]  Dimitris Mourtzis,et al.  Simulation in the design and operation of manufacturing systems: state of the art and new trends , 2019, Int. J. Prod. Res..

[10]  Jay Lee,et al.  Introduction to cyber manufacturing , 2016 .

[11]  Stuart W. Leslie,et al.  Forces of production : a social history of industrial automation , 1985 .

[12]  Kashif Mahmood,et al.  Development of cyber-physical production systems based on modelling Technologies , 2019, Proceedings of the Estonian Academy of Sciences.

[13]  Edward A. Lee,et al.  Introduction to Embedded Systems - A Cyber-Physical Systems Approach , 2013 .

[14]  Keith Jackson,et al.  Digital Manufacturing and Flexible Assembly Technologies for Reconfigurable Aerospace Production Systems , 2016 .

[15]  Toivo Tähemaa,et al.  Adaptive Industrial Robots Using Machine Vision , 2018, Volume 2: Advanced Manufacturing.

[16]  George Chryssolouris,et al.  The digital twin implementation for linking the virtual representation of human-based production tasks to their physical counterpart in the factory-floor , 2018, Int. J. Comput. Integr. Manuf..

[17]  Frédéric Cugnon,et al.  Machine Tool: From the Digital Twin to the Cyber-Physical Systems , 2019 .

[18]  Marcelo V. Garcia,et al.  Human-Robot Collaboration Based on Cyber-Physical Production System and MQTT , 2020 .

[19]  Fiorenzo Franceschini,et al.  A conceptual framework to evaluate human-robot collaboration , 2020, The International Journal of Advanced Manufacturing Technology.

[20]  László Monostori,et al.  ScienceDirect Variety Management in Manufacturing . Proceedings of the 47 th CIRP Conference on Manufacturing Systems Cyber-physical production systems : Roots , expectations and R & D challenges , 2014 .

[21]  Kevin I-Kai Wang,et al.  Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues , 2020, Robotics Comput. Integr. Manuf..

[22]  A. Kusiak Smart manufacturing , 2018, Int. J. Prod. Res..

[23]  Robert X. Gao,et al.  Digital Twin for rotating machinery fault diagnosis in smart manufacturing , 2018, Int. J. Prod. Res..

[24]  Yushun Fan,et al.  Manufacturing process analysis with support of workflow modelling and simulation , 2009 .

[25]  Fei Tao,et al.  IIHub: An Industrial Internet-of-Things Hub Toward Smart Manufacturing Based on Cyber-Physical System , 2018, IEEE Transactions on Industrial Informatics.

[26]  Andrew Y. C. Nee,et al.  Enabling technologies and tools for digital twin , 2019 .

[27]  Marco Sacco,et al.  A Telemetry-driven Approach to Simulate Data-intensive Manufacturing Processes , 2016 .

[28]  Vincent Havard,et al.  Digital twin and virtual reality: a co-simulation environment for design and assessment of industrial workstations , 2019, Production & Manufacturing Research.

[29]  Toivo Tähemaa,et al.  DIGITAL TWIN BASED SYNCHRONISED CONTROL AND SIMULATION OF THE INDUSTRIAL ROBOTIC CELL USING VIRTUAL REALITY , 2019, Journal of Machine Engineering.

[30]  Kashif Mahmood,et al.  IoT-Based Senses for Virtual Enterprises , 2018, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society.

[31]  Kristo Karjust,et al.  Development of a product lifecycle management model based on the fuzzy analytic hierarchy process , 2017 .

[32]  Andrew Kusiak,et al.  Data-driven smart manufacturing , 2018, Journal of Manufacturing Systems.

[33]  François E. Cellier,et al.  Continuous system modeling , 1991 .

[34]  Arne Bilberg,et al.  Digital twins of human robot collaboration in a production setting , 2018 .

[35]  Jon Kepa Gerrikagoitia,et al.  Digital Manufacturing Platforms in the Industry 4.0 from Private and Public Perspectives , 2019, Applied Sciences.

[36]  Reiner Anderl,et al.  Dynamic, Adaptive Worker Allocation for the Integration of Human Factors in Cyber-Physical Production Systems , 2016 .