ABSTRACT
The recent development of new technologies within the Industry 4.0 revolution drives the increased digitization of manufacturing plants. To effectively utilize the digital twins, it is essential to guarantee a correct alignment between the physical system and the associated simulation model along the whole system life cycle. Data assimilation is frequently used to incorporate observation data into a running model to produce improved estimates of state variables of interest. However, it assumes a closed system and cannot handle structural changes in the system, e.g., machine breakdown. Instead of combining the observation data into an existing model, we aim to automatically generate the model concurrently with the data assimilation procedure. This can reduce the time and cost of building the model. In addition, it can generate a more accurate model when sudden operational changes are not reflected at the higher planning levels. Component-based model generation approach is used with the application of data and process mining techniques to generate a complete process model from the data. A new data assimilation method is proposed to iteratively generate new models based on the arrival of further data. Each model is simulated to obtain the system performance, which will be compared to the real system performance to select the best-estimated model. Identical twin experiments of a wafer-fab simulation are conducted under different scenarios to evaluate the feasibility of the proposed approach.
- Fan Bai, Feng Gu, Xiaolin Hu, and Song Guo. 2016. Particle Routing in Distributed Particle Filters for Large-Scale Spatial Temporal Systems. IEEE Transactions on Parallel and Distributed Systems 27, 2 (Feb. 2016), 481–493. https://doi.org/10.1109/TPDS.2015.2405912Google ScholarDigital Library
- Sören Bergmann and Steffen Strassburger. 2010. Challenges for the Automatic Generation of Simulation Models for Production Systems. In Proceedings of the 2010 Summer Computer Simulation Conference. Society for Computer Simulation International, Ottawa, Ontario, Canada, 545–549.Google ScholarDigital Library
- F Bouttier and P Courtier. 2002. Data assimilation concepts and methods March 1999. Meteorological training course lecture series. ECMWF 718 (2002), 59.Google Scholar
- Frank Chance, Jennifer Robinson, and John W. Fowler. 1996. Supporting Manufacturing with Simulation: Model Design, Development, and Deployment. In Proceedings of the 28th Conference on Winter Simulation (Coronado, California, USA) (WSC ’96). IEEE Computer Society, USA, 114–121. https://doi.org/10.1145/256562.256586Google ScholarDigital Library
- Petar M Djuric, Jayesh H Kotecha, Jianqui Zhang, Yufei Huang, Tadesse Ghirmai, Mónica F Bugallo, and Joaquin Miguez. 2003. Particle filtering. IEEE signal processing magazine 20, 5 (2003), 19–38.Google ScholarCross Ref
- Jonas Friederich, Giovanni Lugaresi, Sanja Lazarova-Molnar, and Andrea Matta. 2022. Process Mining for Dynamic Modeling of Smart Manufacturing Systems: Data Requirements. Procedia CIRP 107 (2022), 546–551.Google ScholarCross Ref
- Anahita Farhang Ghahfarokhi, Gyunam Park, Alessandro Berti, and Wil M. P. van der Aalst. 2021. OCEL: A Standard for Object-Centric Event Logs. In New Trends in Database and Information Systems. Springer International Publishing, Cham, 169–175.Google Scholar
- S. Gillijns, O.B. Mendoza, J. Chandrasekar, B.L.R. De Moor, D.S. Bernstein, and A. Ridley. 2006. What is the ensemble Kalman filter and how well does it work?. In 2006 American Control Conference. IEEE, Minneapolis, MN, USA, 6 pp.–. https://doi.org/10.1109/ACC.2006.1657419Google ScholarCross Ref
- Xiaolin Hu and Peisheng Wu. 2019. A Data Assimilation Framework for Discrete Event Simulations. ACM Transactions on Modeling and Computer Simulation 29, 3 (June 2019), 17:1–17:26. https://doi.org/10.1145/3301502Google ScholarDigital Library
- Yilin Huang, Mamadou D. Seck, and Alexander Verbraeck. 2011. From Data to Simulation Models: Component-based Model Generation with a Data-Driven Approach. In Proceedings of the 2011 Winter Simulation Conference (WSC). IEEE, Phoenix, AZ, USA, 3719–3729. https://doi.org/10.1109/WSC.2011.6148065Google ScholarCross Ref
- Nasser Jazdi. 2014. Cyber physical systems in the context of Industry 4.0. In 2014 IEEE international conference on automation, quality and testing, robotics. IEEE, Cluj-Napoca, Romania, 1–4.Google ScholarCross Ref
- David Kayton, Tim Teyner, Christopher Schwartz, and Reha Uzsoy. Fourth Quarter 1997. Focusing Maintenance Improvement Efforts in a Wafer Fabrication Facility Operating under the Theory of Constraints. Production and Inventory Management Journal 38, 4 (Fourth Quarter 1997), 51–57.Google Scholar
- D. Krenczyk, B. Skolud, and M. Olender. 2016. Semi-Automatic Simulation Model Generation of Virtual Dynamic Networks for Production Flow Planning. IOP Conference Series: Materials Science and Engineering 145, 4 (Aug. 2016), 042021. https://doi.org/10.1088/1757-899X/145/4/042021Google Scholar
- Sander JJ Leemans, Erik Poppe, and Moe T Wynn. 2019. Directly follows-based process mining: Exploration & a case study. In 2019 International Conference on Process Mining (ICPM). IEEE, Aachen, Germany, 25–32.Google ScholarCross Ref
- Sander J. J. Leemans, Dirk Fahland, and Wil M. P. van der Aalst. 2013. Discovering Block-Structured Process Models from Event Logs - A Constructive Approach. In Application and Theory of Petri Nets and Concurrency. Vol. 7927. Springer Berlin Heidelberg, Berlin, Heidelberg, 311–329. https://doi.org/10.1007/978-3-642-38697-8_17Google ScholarDigital Library
- Giovanni Lugaresi. 2021. Automated Generation and Exploitation of Discrete Event Simulation Models for Decision Making in Manufacturing. Ph. D. Dissertation. Politecnico di Milano, Milan, Italy.Google Scholar
- Giovanni Lugaresi, Gianluca Aglio, Federico Folgheraiter, and Andrea Matta. 2019. Real-time validation of digital models for manufacturing systems: A novel signal-processing-based approach. In 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE). IEEE, Vancouver, BC, Canada, 450–455.Google ScholarDigital Library
- Giovanni Lugaresi, Sofia Gangemi, Giulia Gazzoni, and Andrea Matta. 2022. Online Validation of Simulation-Based Digital Twins Exploiting Time Series Analysis. In 2022 Winter Simulation Conference (WSC). IEEE, Singapore, 2912–2923.Google Scholar
- Alexander Mages, Carina Mieth, Jens Hetzler, Fadil Kallat, Jakob Rehof, Christian Riest, and Tristan Schäfer. 2022. Automatic Component-Based Synthesis of User-Configured Manufacturing Simulation Models. In 2022 Winter Simulation Conference (WSC). IEEE, Singapore, 1841–1852.Google Scholar
- Lars Mönch, John W Fowler, and Scott J Mason. 2012. Production planning and control for semiconductor wafer fabrication facilities: modeling, analysis, and systems. Vol. 52. Springer Science & Business Media, New York, NY, USA.Google Scholar
- Lucy E Morgan and Russell R Barton. 2022. Fourier trajectory analysis for system discrimination. European Journal of Operational Research 296, 1 (2022), 203–217.Google ScholarCross Ref
- B Nelson 2013. Foundations and methods of stochastic simulation. A first course. International series in operations research & management science 187 (2013), 313 pages.Google Scholar
- Robert G Sargent. 2010. Verification and validation of simulation models. In Proceedings of the 2010 winter simulation conference. IEEE, Baltimore, MD, USA, 166–183.Google ScholarCross Ref
- Denise Maria Vecino Sato, Sheila Cristiana De Freitas, Jean Paul Barddal, and Edson Emilio Scalabrin. 2021. A Survey on Concept Drift in Process Mining. Comput. Surveys 54, 9 (Oct. 2021), 189:1–189:38. https://doi.org/10.1145/3472752Google ScholarDigital Library
- Moon GI Seok, Chew Wye Chan, Wentong Cai, Hessam S. Sarjoughian, and Daejin Park. 2020. Runtime Abstraction-Level Conversion of Discrete-Event Wafer-fabrication Models for Simulation Acceleration. In Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation(SIGSIM-PADS ’20). Association for Computing Machinery, New York, NY, USA, 83–92. https://doi.org/10.1145/3384441.3395982Google ScholarDigital Library
- Young Jun Son and Richard A Wysk. 2001. Automatic Simulation Model Generation for Simulation-Based, Real-Time Shop Floor Control. Computers in Industry 45, 3 (July 2001), 291–308. https://doi.org/10.1016/S0166-3615(01)00086-0Google ScholarDigital Library
- Wil MP van der Aalst. 2010. Process discovery: Capturing the invisible. IEEE Computational Intelligence Magazine 5, 1 (2010), 28–41.Google ScholarDigital Library
- Harry L. Van Trees and Kristine L. Bell. 2007. A Tutorial on Particle Filters for Online Nonlinear/NonGaussian Bayesian Tracking. Wiley-IEEE press, NY, USA, 723–737. https://doi.org/10.1109/9780470544198.ch73Google Scholar
- A. J. M. M. Weijters, Wil M.P. van der Aalst, and Ana K. A. de Medeiros. 2006. Process mining with the HeuristicsMiner algorithm. Technical Report. Technische Universiteit Eindhoven, Eindhoven, Netherlands.Google Scholar
- Xu Xie and Alexander Verbraeck. 2019. A Particle Filter-Based Data Assimilation Framework for Discrete Event Simulations. SIMULATION 95, 11 (Nov. 2019), 1027–1053. https://doi.org/10.1177/0037549718798466Google ScholarDigital Library
- Xu Xie, Alexander Verbraeck, and Feng Gu. 2016. Data Assimilation in Discrete Event Simulations - a Rollback Based Sequential Monte Carlo Approach. In 2016 Symposium on Theory of Modeling and Simulation (TMS-DEVS). IEEE, Pasadena, CA, 1–8. https://doi.org/10.23919/TMS.2016.7918817Google Scholar
Index Terms
- Automatic Model Generation and Data Assimilation Framework for Cyber-Physical Production Systems
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