Integration of Digital Twin with Simulation in Order to Meet Factory Expectations
Pawlewski Paweł 1, Olszewski Adam 2
1 Poznan University of Technology
Faculty of Management Engineering
ul. J. Rychlewskiego 2, 60-965 Poznań, Poland
E-mail: pawel.pawlewski@put.poznan.pl2 Institute of Bioorganic Chemistry
Poznan Supercomputing & Networking Center
ul. Noskowskiego 12/14, 61-704 Poznań, Poland
E-mail: adol@man.poznan.pl
Received:
Received: 22 June 2021; revised: 11 August 2021; accepted: 24 August 2021; published online: 16 September 2021
DOI: 10.12921/cmst.2021.0000020
Abstract:
This article presents the results of a research project carried out in a factory of an aviation manufacturer of aircraft engine parts. The project aimed to design a simulation model for the company’s department producing the factory’s critical items with extensive lead time: approximately 50–60 days. The company’s engineers validated the model by comparing it against historical and reference data for the modeled line. The real-life sequence was used as a reference for simulation experiments. Two sequences with shorter lead times have been found. Results of the project inspired the company to redefine its approach to management by preparing dynamic production plans adaptable to environment variables. Based on the simulation project, a conceptual method of proceeding was proposed enabling the introduction of such a task. The concept proposed restructuring the factory, defining observation points and integrating the digital twin along with “What-If” simulation experiments. By distinguishing between the location and operation observation points one can map the real life processes onto the simulation model. Consequently, experiments can be launched, simulating possible scenarios starting from a predefined moment of the actual real life process. Also the benefits resulting from the application of the proposed solution were defined.
Key words:
computer simulation, digital twin, making decision, manufacturing
References:
[1] K. Schwab, The Fourth Industrial Revolution, Crown Business (2017).
[2] C. Pehlivan, R. Efeoglu, Empirical investigation of technology-Industry 4.0 relation of the effect on trade, Economics, Finance, Politics 14(4), 1490 (2019).
[3] M. Hermann, T. Pentek, B. Otto, Design principles for industrie 4.0 scenarios, Proceedings of the Annual Hawaii International Conference on System Sciences 2016 (2016).
[4] H. Bauer, F. Brandl, C. Lock, G. Reinhar, Integration of Industrie 4.0 in Lean Manufacturing Learning Factories, Procedia Manufacturing 23, 147–152 (2018).
[5] W. Kagermann, W. Wahister, J. Helbig, Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the Future of German Manufacturing Industry (2013).
[6] A. Fuller, Z. Fan, Ch. Day, Ch. Barlow, Digital Twin: Enabling Technologies, Challenges and Open Research, IEEE Access 8, 108952–108971 (2020).
[7] C. Cimino, E. Negri, L. Fumagalli, Review of digital twin applications in manufacturing, Computers in Industry 113 (2019).
[8] S. Haag, R. Anderl, Automated Generation of as-manufactured geometric Representations for Digital Twins using STEP, Procedia CIRP 84 (2019).
[9] M. Grieves, Digital Twin: Manufacturing Excellence Through Virtual Factory Replication, Florida Institute of Technology, White Paper 1, 1–7 (2014).
[10] H. van der Valk, H. Haße, F. Möller, M. Arbter, J.-L. Henning, B. Otto, A taxonomy of digital twins, 26th Americas Conference on Information Systems, AMCIS 2020 (2020).
[11] S.N. Grigoriev, V.A. Dolgov, P.A. Nikishechkin, N.V. Dolgov, Information model of production and logistics systems of machine-building enterprises as the basis for the development and maintenance of their digital twins, [In:] IOP Conference Series: Materials Science and Engineering 971, 3 (2020).
[12] M. Beaverstock, A. Greenwood, W. Nordgren, Applied Simulation. Modeling and Analysis using Flexsim, Flexsim Software Products, Inc., Canyon Park Technology Center, Orem, USA (2017).
[13] P. Pawlewski, K. Kluska, Modeling and simulation of bus assembling process using DES/ABS approach, ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, Salamanca 6, 59–72 (2017).
[14] P. Pawlewski, Methodology For Layout and Intralogistics Redesign Using Simulation, Proceedings of the 2018 Winter Simulation Conference, Eds. M. Rabe, A.A. Juan, N. Mustafee, A. Skoogh, S. Jain, B. Johansson (2018).
[15] P. Pawlewski, Built-In Lean Management Tools in Simulation Modeling, Proceedings of the 2019 Winter Simulation Conference, Eds. N. Mustafee, K.-H.G. Bae, S. Lazarova-Molnar, M. Rabe, C. Szabo, P. Haas, Y.-J. Son (2019).
This article presents the results of a research project carried out in a factory of an aviation manufacturer of aircraft engine parts. The project aimed to design a simulation model for the company’s department producing the factory’s critical items with extensive lead time: approximately 50–60 days. The company’s engineers validated the model by comparing it against historical and reference data for the modeled line. The real-life sequence was used as a reference for simulation experiments. Two sequences with shorter lead times have been found. Results of the project inspired the company to redefine its approach to management by preparing dynamic production plans adaptable to environment variables. Based on the simulation project, a conceptual method of proceeding was proposed enabling the introduction of such a task. The concept proposed restructuring the factory, defining observation points and integrating the digital twin along with “What-If” simulation experiments. By distinguishing between the location and operation observation points one can map the real life processes onto the simulation model. Consequently, experiments can be launched, simulating possible scenarios starting from a predefined moment of the actual real life process. Also the benefits resulting from the application of the proposed solution were defined.
Key words:
computer simulation, digital twin, making decision, manufacturing
References:
[1] K. Schwab, The Fourth Industrial Revolution, Crown Business (2017).
[2] C. Pehlivan, R. Efeoglu, Empirical investigation of technology-Industry 4.0 relation of the effect on trade, Economics, Finance, Politics 14(4), 1490 (2019).
[3] M. Hermann, T. Pentek, B. Otto, Design principles for industrie 4.0 scenarios, Proceedings of the Annual Hawaii International Conference on System Sciences 2016 (2016).
[4] H. Bauer, F. Brandl, C. Lock, G. Reinhar, Integration of Industrie 4.0 in Lean Manufacturing Learning Factories, Procedia Manufacturing 23, 147–152 (2018).
[5] W. Kagermann, W. Wahister, J. Helbig, Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the Future of German Manufacturing Industry (2013).
[6] A. Fuller, Z. Fan, Ch. Day, Ch. Barlow, Digital Twin: Enabling Technologies, Challenges and Open Research, IEEE Access 8, 108952–108971 (2020).
[7] C. Cimino, E. Negri, L. Fumagalli, Review of digital twin applications in manufacturing, Computers in Industry 113 (2019).
[8] S. Haag, R. Anderl, Automated Generation of as-manufactured geometric Representations for Digital Twins using STEP, Procedia CIRP 84 (2019).
[9] M. Grieves, Digital Twin: Manufacturing Excellence Through Virtual Factory Replication, Florida Institute of Technology, White Paper 1, 1–7 (2014).
[10] H. van der Valk, H. Haße, F. Möller, M. Arbter, J.-L. Henning, B. Otto, A taxonomy of digital twins, 26th Americas Conference on Information Systems, AMCIS 2020 (2020).
[11] S.N. Grigoriev, V.A. Dolgov, P.A. Nikishechkin, N.V. Dolgov, Information model of production and logistics systems of machine-building enterprises as the basis for the development and maintenance of their digital twins, [In:] IOP Conference Series: Materials Science and Engineering 971, 3 (2020).
[12] M. Beaverstock, A. Greenwood, W. Nordgren, Applied Simulation. Modeling and Analysis using Flexsim, Flexsim Software Products, Inc., Canyon Park Technology Center, Orem, USA (2017).
[13] P. Pawlewski, K. Kluska, Modeling and simulation of bus assembling process using DES/ABS approach, ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, Salamanca 6, 59–72 (2017).
[14] P. Pawlewski, Methodology For Layout and Intralogistics Redesign Using Simulation, Proceedings of the 2018 Winter Simulation Conference, Eds. M. Rabe, A.A. Juan, N. Mustafee, A. Skoogh, S. Jain, B. Johansson (2018).
[15] P. Pawlewski, Built-In Lean Management Tools in Simulation Modeling, Proceedings of the 2019 Winter Simulation Conference, Eds. N. Mustafee, K.-H.G. Bae, S. Lazarova-Molnar, M. Rabe, C. Szabo, P. Haas, Y.-J. Son (2019).
