TOWARDS DIGITAL TWIN-DRIVEN PERFORMANCE EVALUATION METHODOLOGY OF FMS

Grzegorz BOCEWICZ

grzegorz.bocewicz@tu.koszalin.pl
Faculty of Electronics and Computer Science, Koszalin University of Technology (Poland)

Robert WÓJCIK


Faculty of Information and Communication Technology, Wrocław University of Science and Technology, (Poland)

Paweł SITEK


Department of Information Systems, Kielce University of Technology, Kielce (Poland)

Zbigniew BANASZAK


Faculty of Electronics and Computer Science, Koszalin University of Technology (Poland)

Abstract

The paper presents a method of automated modelling and performance evaluation of concurrent production flows carried out in Flexible Manufacturing Systems. The method allows for quick assessment of various variants of such systems, considering their structure and the organization of production flow of possible ways of their implementation. Its essence is the conditions imposed on the designed model, limiting the space of possible variants of the production flow only to deadlock-free variants. The practical usefulness of the model implemented in the proposed method illustrates the example, which describes the simultaneous assessment of alternative variants of the flexible machining module's structure and the planned multi-assortment production. The ability of the method to focus on feasible solutions offers attractive perspectives for guiding the Digital Twin-like scenario in situations caused by the need to change the production flow.


Keywords:

FMS, Petri Nets, Performance evaluation

Alexopoulos, K., Anagiannis, I., Nikolakis, N., & Chryssolouris, G. (2022). A quantitative approach to resilience in manufacturing systems. International Journal of Production Research, 60(13), 4342–4360.
DOI: https://doi.org/10.1080/00207543.2021.2018519   Google Scholar

Bakar, B. A., Henry, R. M., & Ali, M. (1991). An alternative approach in batch process control implementation using hierarchical Petri nets, World Scientific. Proc. of the International Conference on Computer Integrated Manufacturing (pp. 171–174).
  Google Scholar

Banaszak, Z. (1992). Synchronisation of robots in flexible assembly systems. Archiwum Budowy Maszyn, 39(1–2), 117–133.
  Google Scholar

Banaszak, Z., Skolud, B., & Zaremba, M. B. (2003). Computer-aided prototyping of production flows for virtual enterprise. Journal of Intelligent Manufacturing, 14, 83–106.
DOI: https://doi.org/10.1023/A:1022291313614   Google Scholar

Bocewicz, G., Wójcik, R., Witczak, M., & Banaszak, Z. (2022). Petri Net Approach to Automated Modelling and Performance Evaluation for Robotic Assembly Systems. 2022 26th International Conference on Methods and Models in Automation and Robotics (MMAR) (pp. 306–311). IEEE. https://doi.org/10.1109/MMAR55195.2022.9874291
DOI: https://doi.org/10.1109/MMAR55195.2022.9874291   Google Scholar

Bujari, A., Calvio, A., Foschini, L., Sabbioni, A., & Corradi, A. (2021). A Digital Twin Decision Support System for the Urban Facility Management Process. Sensors, 21(24), 8460. https://doi.org/10.3390/s21248460
DOI: https://doi.org/10.3390/s21248460   Google Scholar

Claes, D., & Tuyls, K. (2018). Multi robot collision avoidance in a shared workspace. Autonomous Robots, 42, 1749–1770. https://doi.org/10.1007/s10514-018-9726-5
DOI: https://doi.org/10.1007/s10514-018-9726-5   Google Scholar

Coito, T., Faria, P., Martins, M. S. E., Firme, B., Vieira, S. M., Figueiredo, J., & Sousa, J. M. C. (2022). Digital Twin of a Flexible Manufacturing System for Solutions Preparation. Automation, 3(1), 153–175. https://doi.org/10.3390/automation3010008
DOI: https://doi.org/10.3390/automation3010008   Google Scholar

David, J., Lobov, A., & Lanz, M. (2018). Leveraging Digital Twins for Assisted Learning of Flexible Manufacturing Systems. 2018 IEEE 16th International Conference on Industrial Informatics (INDIN) (pp. 529-535). IEEE. https://doi.org/10.1109/INDIN.2018.8472083
DOI: https://doi.org/10.1109/INDIN.2018.8472083   Google Scholar

Hatono, I., Katoh, N., Yamagata, K., & Tamura, H. (1989). Modelling of FMS under uncertainty using stochastic Petri Nets. Proc. of the 3rd International Workshop on Petri nets and performance models (pp. 122–130).
  Google Scholar

He, Z., Zhang, R., Ran, N., & Gu, C. (2022). Path Planning of Multi-Type Robot Systems with Time Windows Based on Timed Colored Petri Nets. Applied Science, 12(14), 6878. https://doi.org/10.3390/app12146878
DOI: https://doi.org/10.3390/app12146878   Google Scholar

Heiner, M. (1992). Petri net based software validation (prospects and limits), Technical report No. TR-92-022. International Computer Science Institute.
  Google Scholar

Janardhanan, M. N., Li, Z., Bocewicz, G., Banaszak, Z., & Nielsen, P. (2019). Metaheuristic Algorithms for balancing robotic assembly lines with sequence-dependent robot setup times. Applied Mathematical Modelling, 65, 256–270.
DOI: https://doi.org/10.1016/j.apm.2018.08.016   Google Scholar

Jensen, K. (1987). Computer tools for construction, modification and analysis of Petri nets. Lecture Notes on Computer Science (No. 255). Springer Verlag.
DOI: https://doi.org/10.1007/3-540-17906-2_20   Google Scholar

Jonsson, P. (2000). An empirical taxonomy of advanced manufacturing technology. International Journal of Operations & Production Management, 20(12), 1446–1474.
DOI: https://doi.org/10.1108/01443570010353103   Google Scholar

Laemmle, A., & Gust, S. (2019). Automatic layout generation of robotic production cells in a 3D manufacturing simulation environment. Procedia CIRP, 84, 316–321.
DOI: https://doi.org/10.1016/j.procir.2019.04.207   Google Scholar

Makris, S., Michalos, G., & Chryssolouris, G. (2012). Virtual Commissioning of an Assembly Cell with Cooperating Robots. Advances in Decision Sciences, 2012, 428060. https://doi.org/10.1155/2012/428060
DOI: https://doi.org/10.1155/2012/428060   Google Scholar

Manu, G., Kumar, V. M., Nagesh, H., Jagadeesh, D., & Gowtham, M. B. (2018). Flexible Manufacturing Systems (FMS): A Review. International Journal of Mechanical and Production Engineering Research and Development, 8(2), 323–336.
DOI: https://doi.org/10.24247/ijmperdapr201836   Google Scholar

Neto, A. A., Carrijoa B. S., Brock, J. G. R, Deschamps, F., & Lima, E. P. (2021). Digital twin-driven decision support system for opportunistic preventive maintenance scheduling in manufacturing. Procedia Manufacturing, 55, 439–446.
DOI: https://doi.org/10.1016/j.promfg.2021.10.060   Google Scholar

Nielsen, L. D., Sung, I., & Nielsen, P. (2019). Convex Decomposition for a Coverage Path Planning for Autonomous Vehicles: Interior Extension of Edges. Sensors, 19(19), 4165. https://doi.org/10.3390/s19194165
DOI: https://doi.org/10.3390/s19194165   Google Scholar

Nielsen, P., Michna, Z., & Do, N. A. D. (2014). An Empirical Investigation of Lead Time Distributions. Advances in Production Management Systems. Innovative and Knowledge-Based Production Management in a Global-Local World. APMS 2014. IFIP Advances in Information and Communication Technology (vol. 438). Springer. https://doi.org/10.1007/978-3-662-44739-0_53
DOI: https://doi.org/10.1007/978-3-662-44739-0_53   Google Scholar

Patalas-Maliszewska, J., & Kłos, S. (2019). An Approach to Supporting the Selection of Maintenance Experts in the Context of Industry 4.0. Applied Sciences, 9(9), 1848. https://doi.org/10.3390/app9091848
DOI: https://doi.org/10.3390/app9091848   Google Scholar

Rachamadugu, R., & Stecke, K. E. (1994). Classification and review of FMS scheduling procedures. Production Planning & Control, 5(1), 2–20. https://doi.org/10.1080/09537289408919468
DOI: https://doi.org/10.1080/09537289408919468   Google Scholar

Recalde, L., Silva, M., Ezpeleta, J., & Teruel, E. (2022). Petri Nets and Manufacturing Systems: An ExamplesDriven Tour. ACPN 2003. Lecture Notes in Computer Science (vol. 3098). Springer. https://doi.org/10.1007/978-3-540-27755-2_21
DOI: https://doi.org/10.1007/978-3-540-27755-2_21   Google Scholar

Reisig, W. (1982). Petri nets. Springer Verlag. Reutenauer, Ch. (1988). The mathematics of Petri nets. Englewood Cliffs.
  Google Scholar

Silva, E. B., Costa, M. G., Silva, M. F., & Pereira, F. H. (2012). Evaluation of Production Sequencing Rules in Job Shop and Flow Shop Environment through Computer Simulation. ICIEOM 2012 (no. 257).
  Google Scholar

Sliwa, M., & Patalas-Maliszewska, J. (2016). A Strategic Knowledge Map for the Research and Development Department in a Manufacturing Company. Foundations of Management, 8(1), 151–166.
DOI: https://doi.org/10.1515/fman-2016-0012   Google Scholar

Stączek, P., Pizoń, J., Danilczuk, W., & Gola, A. (2021). A digital twin approach for the improvement of an autonomous mobile robots (AMR's) operating environment – a case study. Sensors, 21(23), 7830. https://doi.org/10.3390/s21237830
DOI: https://doi.org/10.3390/s21237830   Google Scholar

Świć, A., & Gola, A. (2013). Economic Analysis of Casing Parts Production in a Flexible Manufacturing System. Actual Problems of Economics, 141(3), 526–533.
  Google Scholar

Vaisi, B. (2022). A review of optimization models and applications in robotic manufacturing systems: Industry 4.0 and beyond. Decision Analytics Journal, 2, 100031. https://doi.org/10.1016/j.dajour.2022.100031
DOI: https://doi.org/10.1016/j.dajour.2022.100031   Google Scholar

Van der Aalst, W. M. (1992). Timed coloured Petri nets and their application to logistics. Technische Universiteit Eindhoven.
DOI: https://doi.org/10.1007/3-540-56863-8_61   Google Scholar

Viswandham, N., & Narahari, Y. (1992). Performance modelling of automated manufacturing systems. Prentice-Hall.
  Google Scholar

Yang, B., & Hu, H. (2022). Maximally Permissive Deadlock and Livelock Avoidance for Automated Manufacturing Systems via Critical Distance. In IEEE Transactions on Automation Science and Engineering. IEEE. https://doi.org/10.1109/TASE.2021.3138169
DOI: https://doi.org/10.1109/TASE.2021.3138169   Google Scholar

Zanchettin, A. M. (2021). Robust scheduling and dispatching rules for high-mix collaborative manufacturing systems. Flexible Services and Manufacturing Journal, 34, 293–316. https://doi.org/10.1007/s10696-021-09406-x
DOI: https://doi.org/10.1007/s10696-021-09406-x   Google Scholar

Zhang, F., Bai, J., & Yang, D. (2022). Digital twin data-driven proactive job-shop scheduling strategy towards asymmetric manufacturing execution decision. Scientific Reports, 12, 1546. https://doi.org/10.1038/s41598-022-05304-w
DOI: https://doi.org/10.1038/s41598-022-05304-w   Google Scholar

Zhou, K. Q., & Zain, A. M. (2016). Fuzzy Petri nets, and industrial applications: a review. Artificial Intelligence Review, 45(4), 405–446. https://doi.org/10.1007/s10462-015-9451-9
DOI: https://doi.org/10.1007/s10462-015-9451-9   Google Scholar

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Published
2022-09-30

Cited by

BOCEWICZ, G., WÓJCIK, R., SITEK, P., & BANASZAK, Z. (2022). TOWARDS DIGITAL TWIN-DRIVEN PERFORMANCE EVALUATION METHODOLOGY OF FMS. Applied Computer Science, 18(3), 5–18. https://doi.org/10.35784/acs-2022-17

Authors

Grzegorz BOCEWICZ 
grzegorz.bocewicz@tu.koszalin.pl
Faculty of Electronics and Computer Science, Koszalin University of Technology Poland

Authors

Robert WÓJCIK 

Faculty of Information and Communication Technology, Wrocław University of Science and Technology, Poland

Authors

Paweł SITEK 

Department of Information Systems, Kielce University of Technology, Kielce Poland

Authors

Zbigniew BANASZAK 

Faculty of Electronics and Computer Science, Koszalin University of Technology Poland

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