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2022 | OriginalPaper | Buchkapitel

3. Digital Twin: A Conceptual View

verfasst von : Josip Stjepandić, Markus Sommer, Sebastian Stobrawa

Erschienen in: DigiTwin: An Approach for Production Process Optimization in a Built Environment

Verlag: Springer International Publishing

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Abstract

Over the last few years, a concept called Digital Twin has evolved rapidly as a new key approach in the field of Product Lifecycle Management (PLM). Briefly, a Digital Twin is a digital representation of an active unique product or unique product-service-system with its selected characteristics within dedicated lifecycle phases. This concept has experienced a tremendous impact by IoT technology, which has drastically reduced the costs. It builds the foundation not only for connected products and services but also for entirely new offerings and business models. Three main characteristics of Digital Twin were identified: representation of a physical system, bidirectional data exchange, and the connection along the entire lifecycle. For a better understanding, three subtypes of Digital Twin are presented, namely: The Digital Master, the Digital Manufacturing Twin, and the Digital Instance Twin which refer to the different phases of the product lifecycle: design, production and operation. Therefore, this chapter formulates a consistent and detailed definition of Digital Twins and gives insight in dedicated research direction. Finally, based on the Digital Twin characteristics, an approach for generation of Digital Twin in manufacturing is shown.

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Vorheriges Kapitel Requirements for the Optimization of Processes Using a Digital Twin of Production Systems

Nächstes Kapitel Scan Methods and Tools for Reconstruction of Built Environments as Basis for Digital Twins

Trauer J, Schweigert-Recksiek S, Engel C, Spreitzer K and Zimmermann M (2020) What is a Digital Twin? Definitions and insights from an industrial case study in technical product development. Int Design Conf Design 2020. https://doi.org/10.1017/dsd.2020.15

Sieg O, Völl, C (2020) Digital twin and Product Lifecycle Management (PLM) as part of the corporate strategy [Digital Twin und PLM als Teil der Unternehmensstrategie]. ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb 115:38

Stark R, Damerau T (2019) Digital Twin. In: Chatti S, Laperrière L, Reinhart G, Tolio T (eds) The international academy for production engineering. CIRP encyclopedia of production engineering, 2nd edn. Springer, Berlin Heidelberg. https://doi.org/10.1007/978-3-642-35950-7_16870-1

Lo CK, Chen CH, Zhong RY (2021) A review of Digital Twin in product design and development. Adv Eng Inform

Ríos J, Morate FM, Oliva M, Hernández JC (2016) Framework to support the aircraft digital counterpart concept with an industrial design view. Int J Agile Syst Manage 9(3):212–231. https://doi.org/10.1504/IJASM.2016.079934 CrossRef

Ladj A, Wang Z, Meski O, Belkadi F, Ritou M, Da Cunha C (2020) A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective. J Manuf Syst. https://doi.org/10.1016/j.jmsy.2020.07.018 CrossRef

Weber U, Hong NA, Schäfer V, Peters P, Vettermann S (2015) Synced factory Twins: next lever digital manufacturing. ProduktDataJournal 2(2015):8–13

Biesinger F, Meike D, Kraß B, Weyrich M (2018) A case study for a digital twin of body-in-white production systems—general concept for automated updating of planning projects in the digital factory. In: 23rd international conference on emerging technologies and factory automation (ETFA). IEEE, pp 19–26. https://doi.org/10.1109/ETFA.2018.8502467

Donoghue I, Hannola L, Papinniemi J, Mikkola A (2018) The benefits and impact of digital twins in product development phase of PLM. In: Chiabert P, Bouras A, Noël F, Ríos J (eds) PLM 2018: product lifecycle management to support industry 4.0. Springer International Switzerland, pp 432–441. https://doi.org/10.1007/978-3-030-01614-2_40.

10.

Stark J (2015) Product lifecycle management, vol 1. Springer International Publishing, Switzerland. https://doi.org/10.1007/978-3-319-17440-2

11.

Wagner L (2020) Kein Digital Twin ohne digitale Durchgängigkeit. Zeitschrift für den wirtschaftlichen Fabrikbetrieb, Band 115, April 2020, pp 43–45. https://doi.org/10.3139/104.112315

12.

da Silva Serapião Leal G, Guédria W, Panetto H (2019) An ontology for interoperability assessment: a systemic approach. J Indus Inform Integr 16:100100. https://doi.org/10.1016/j.jii.2019.07.001

13.

Ruppert T, Abonyi J (2020) Integration of real-time locating systems into digital twins. J Indus Inform Integr 20:100174. https://doi.org/10.1016/j.jii.2020.100174

14.

El-khoury J, Berezovskyi A, Nyberg A (2019) An industrial evaluation of data access techniques for the interoperability of engineering software tools. J Indus Inform Integr 15(2019):58–68. https://doi.org/10.1016/j.jii.2019.04.004 CrossRef

15.

Haenlein M, Kaplan A, Tan CW, Zhang P (2019) Artificial intelligence (AI) and management analytics. J Manage Anal 6(4):341–343. https://doi.org/10.1080/23270012.2019.1699876 CrossRef

16.

Schmitt J, Bönig J, Borggräfe T, Beitinger G, Deuse, J (2020) Predictive model-based quality inspection using machine learning and edge cloud computing. Adv Eng Inform 45:101101. https://doi.org/10.1016/j.aei.2020.101101

17.

Li C, Zhang S, Qin Y, Estupinan E (2020) A systematic review of deep transfer learning for machinery fault diagnosis. Neurocomputing 407:121–135. https://doi.org/10.1016/j.neucom.2020.04.045 CrossRef

18.

Voell C, Chatterjee P, Rauch A, Golovatchev J (2018) How Digital Twins enable the next level of PLM: a guide for the concept and the implementation in the internet of everything era. In: Chiabert P et al. (eds) Product lifecycle management to support industry 4.0. Springer International, Cham, pp 238–249. https://doi.org/10.1007/978-3-030-01614-2_22.

19.

Xu J, Guo T (2021) Application and research on digital twin in electronic cam servo motion control system. Int J Adv Manuf Technol 112:1145–1158. https://doi.org/10.1007/s00170-020-06553-7 CrossRef

20.

Martínez GS, Sierla SA, Karhela TA, Lappalainen J, Vyatkin V (2018) Automatic generation of a high-fidelity dynamic thermal-hydraulic process simulation model from a 3D plant model. IEEE Access 6:45217–45232. https://doi.org/10.1109/ACCESS.2018.2865206 CrossRef

21.

Gehrke I, Schauss M, Küsters D, Gries T (2020) Experiencing the potential of closed-loop PLM systems enabled by industrial internet of things. Procedia Manuf 45:177–182. https://doi.org/10.1016/j.promfg.2020.04.091 CrossRef

22.

Biahmou A, Emmer C, Pfouga A, Stjepandić J (2016) Digital master as an enabler for industry 4.0. Adv Transdisciplinary Eng 4:672–681. https://doi.org/10.3233/978-1-61499-703-0-672

23.

Sommer M, Stjepandić J, Stobrawa S, von Soden M (2019) Automatic generation of digital twin based on scanning and object recognition. Adv Transdisciplinary Eng 10(2019):645–654. https://doi.org/10.3233/ATDE190174 CrossRef

24.

Utzig S, Kaps R, Azeem SM, Gerndt A (2019) Augmented reality for remote collaboration in aircraft maintenance tasks. 2019 IEEE aerospace conference. https://doi.org/10.1109/AERO.2019.8742228

25.

Fukuda S, Lulić Z, Stjepandić J (2013) FDMU-functional spatial experience beyond DMU? In: Proceedings of the 20th ISPE international conference on concurrent engineering, CE 2013. IOS Press, Amsterdam, 2013, pp 431–440. https://doi.org/10.3233/978-1-61499-302-5-431

26.

Yao J, Zhang Z (2020) C3+ATO function simulation and verification analysis based on timed automata. Adv Transdisciplinary Eng 14:166–173. https://doi.org/10.3233/ATDE200229 CrossRef

27.

Peng Y, Zhao S, Wang H (2021) A digital twin based estimation method for health indicators of DC-DC converters. IEEE Trans Power Electron 36(2):9141430. https://doi.org/10.1109/TPEL.2020.3009600

28.

Besbes M, Zolghadri M, Costa Affonso R, Masmoudi F, Haddar M (2020) 3D facility layout problem. J Intell Manuf. https://doi.org/10.1007/s10845-020-01603-z CrossRef

29.

Usuga Cadavid JP, Lamouri S, Grabot B, Pellerin R, Fortin A (2020) Machine learning applied in production planning and control: a state‑of‑the‑art in the era of industry 4.0. J Intell Manuf. https://doi.org/10.1007/s10845-019-01531-7

30.

Donhauser T, Endter U, Schmidt T, Baier L, Schuderer P, Franke J (2020) Real-time image of production. Material flow simulation during operation | [Betriebsbegleitende materialflusssimulationechtzeitabbild der produktion]. WT Werkstattstechnik 110(4):214–219

31.

He B, Cao X, Hua Y (2021) Data fusion-based sustainable digital twin system of intelligent detection robotics. J Cleaner Prod 280:124181. https://doi.org/10.1016/j.jclepro.2020.124181

32.

Uhlenbrock L, Jensch C, Tegtmeier M, Strube J (2020) Digital twin for extraction process design and operation. Processes 8(7):866. https://doi.org/10.3390/pr8070866 CrossRef

33.

Wallis R, Stjepandic J, Rulhoff S, Stromberger F, Deuse J (2014) Intelligent utilization of digital manufacturing data in modern product emergence processes. Adv Transdisciplinary Eng 261–270. https://doi.org/10.3233/978-1-61499-440-4-261

34.

Lu Y, Xu X (2019) Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services. Robot Comput Integr Manuf 57:92–102. https://doi.org/10.1016/j.rcim.2018.11.006 CrossRef

35.

Peruzzini M, Stjepandić J (2018) Editorial to the special issue “Transdisciplinary analytics in supply chain management.” J Manage Anal 5(2):75–80. https://doi.org/10.1080/23270012.2018.1443405 CrossRef

36.

Barrios P, Eynard B, Danjou C (2018) Towards a digital thread between industrial internet of things and product lifecycle management: experimental work for prototype implementation. In: Chiabert P et al. (eds) Product lifecycle management to support industry 4.0. Springer International, Cham, pp 273–282. https://doi.org/10.1007/978-3-030-42250-9_26

37.

Barata J, Pereira V, Coelho M (2020) Product biography information system: a lifecycle approach to Digital Twins. IEEE Trans Syst Man Cybern Syst 899–904. https://doi.org/10.1109/SMC42975.2020.9283061

38.

van der Valk H, Haße H, Möller F, Arbter M, Henning JL, Otto B (2020) A taxonomy of Digital Twins. In: Anderson B, Thatcher J, Meservy R (eds) Proceedings of the 26th Americas conference on information systems, pp 1–10

39.

Bicocchi N, Cabri G, Mandreoli F, Mecella M (2019) Dynamic digital factories for agile supply chains: an architectural approach. J Indus Inform Integr 15:111–121. https://doi.org/10.1016/j.jii.2019.02.001 CrossRef

40.

Xu Y, Päivärinta T, Kuvaja P (2020) Digital Twins as software and service development ecosystems in industry 4.0: towards a research agenda. Communications in computer and information science, 1210 CCIS, pp 51–64. https://doi.org/10.1007/978-981-15-7530-3_5

41.

DIN SPEC 91345 (2016) Referenzarchitekturmodell Industrie 4.0 (RAMI4.0). Deutsches Institut für Normung (DIN) e.V. https://doi.org/10.31030/2436156

42.

Deuter A, Pethig F (2019) The Digital Twin theory—Eine neue Sicht auf ein Modewort. Industrie 4.0 Management 35(1):27–30. https://doi.org/10.30844/I40M_19-1_S27-30

43.

Ma X, Tao F, Zhang M, Wang T, Zuo Y (2019) Digital twin enhanced human-machine interaction in product lifecycle. Procedia CIRP 83:789–793. https://doi.org/10.1016/j.procir.2019.04.330 CrossRef

44.

Adamenko D, Kunnen S, Pluhnau R, Loibl A, Nagarajah A (2020) Review and comparison of the methods of designing the Digital Twin. Procedia CIRP 91:27–32. https://doi.org/10.1016/j.procir.2020.02.146 CrossRef

45.

Lu Q, Chen L, Li S, Pitta M (2020) Semi-automatic geometric digital twinning for existing buildings based on images and CAD drawings. Autom Construct 115:103183. https://doi.org/10.1016/j.autcon.2020.103183

46.

Denkena B, Dittrich M-A, Stobrawa S, Stjepandic J (2019) Automated generation of a digital twin using scan and object detection for data acquisition, 18. ASIM Fachtagung: Simulation in Produktion Und Logistik, Wissenschaftliche Scripten, Auerbach, pp 49–58

47.

VDI-Richtline 3633, Blatt 1 (2014) Simulation von Logistik-, Materialfluss- und Produktionssystemen - Grundlagen. Beuth, Berlin

48.

Gutenschwager K, Rabe M, Spieckermann S, Wenzel S (2017) Simulation in Produktion und Logistik - Grundlagen und Anwendungen. Springer Vieweg, Wolfenbüttel,. https://doi.org/10.1007/978-3-662-55745-7 CrossRef

49.

Emmer C, Fröhlich A, Stjepandić J (2013) Advanced engineering visualization with standardized 3D formats, IFIP advances in information and communication technology, vol 409. Springer, Berlin Heidelberg, pp 584–595. https://doi.org/10.1007/978-3-642-41501-2_58

50.

Pfouga A, Stjepandić J (2018) Leveraging 3D geometric knowledge in the product lifecycle based on industrial standards. J Comput Des Eng 5(1):54–67. https://doi.org/10.1016/j.jcde.2017.11.002 CrossRef

51.

Ktari A, El Mansori M (2020) Digital twin of functional gating system in 3D printed molds for sand casting using a neural network. J Intell Manuf. https://doi.org/10.1007/s10845-020-01699-3 CrossRef

52.

Wenzel S, Peter T (2017) Prozess-Simulation in kleinen und mittleren Unternehmen mittels des Bausteinkastensystems KMUSimMetall. Simul Produktion und Logistik 2017:433–442

53.

Spieckermann S (2005) Diskrete, ereignisorientierte Simulation in Produktion und Logistik - Herausforderungen und Trends. Simulation und Visualisierung 2005 - SimVis. SCS Publishing House, Erlangen, pp 3–14

54.

Rabe M, Spieckermann S, Wenzel S (2008) Verifikation und Validierung für die Simulation in Produktion und Logistik: Vorgehensmodelle und Techniken. Springer, Berlin,. https://doi.org/10.1007/978-3-540-35282-2 CrossRef

55.

Wenzel S, Weiß M, Collisi-Böhmer S, Pitsch H, Rose O (2007) Qualitätskriterien für die Simulation in Produktion und Logistik: Planung und Durchführung von Simulationsstudien. Springer-Verlag. https://doi.org/10.1007/978-3-540-35276-1 CrossRef

56.

Nyhuis P, Wiendahl HP (2012) Logistische Kennlinien: Grundlagen, Werkzeuge und Anwendungen. Springer, Berlin. https://doi.org/10.1007/978-3-540-92839-3

57.

Bierschenk S, Ritter A, Kuhlmann T (2005) Stand der Digitalen Fabrik bei kleinen und mittelständischen Unternehmen. Fraunhofer-IRB-Verlag

58.

Bischoff J et al. (2015) Erschließung der Potenziale der Anwendungen von, „Industrie 4.0“ im Mittelstand. Studie im Auftrag des Bundesministeriums für Wirtschaft und Energie (BMWi). Mülheim an der Ruhr

59.

Denkena B, Winter F, Hauck S (2016) Anlernen von ablauforientierten simulationsmodellen. Productivity 21(3):52–54

60.

Weissman A, Wegerer S (2019) Unternehmen 4.0: Wie Digitalisierung Unternehmen & Management verändert. In: Erner M (eds) Management 4.0—Unternehmensführung im digitalen Zeitalter. Springer-Verlag, Berlin, pp 43–78. https://doi.org/10.1007/978-3-662-57963-3_2

61.

Sommer M, Stjepandić J, Stobrawa S, von Soden M (2021) Automated generation of a Digital Twin in manufacturing for a built environment using scan and object detection. J Indus Inform Integr (in press)

62.

Denkena B, Dittrich MA, Stobrawa S, Stjepandić J (2020) Efficient generation of a digital twin using object detection for data acquisition and XML-interface for model creation. Procedia CIRP 93:274–279. https://doi.org/10.1016/j.procir.2020.03.016 CrossRef

63.

Moon D, Chung S, Kwon S, Seo J, Shin J (2019) Comparison and utilization of point cloud generated from photogrammetry and laser scanning: 3D world model for smart heavy equipment planning. Autom Construct 98:322–331. https://doi.org/10.1016/j.autcon.2018.07.020 CrossRef

64.

Wang K, Kim M-K (2019) Applications of 3D point cloud data in the construction industry: a fifteen-year review from 2004 to 2018. Adv Eng Inform 39:306–319. https://doi.org/10.1016/j.aei.2019.02.007

65.

Ma Z, Liu S (2018) A review of 3D reconstruction techniques in civil engineering and their applications. Adv Eng Inform 37:163–174. https://doi.org/10.1016/j.aei.2018.05.005 CrossRef

66.

Bondar S, Salem B, Stjepandić J (2018) Indoor object reconstruction based on acquisition by low-cost devices. Adv Transdisciplinary Eng 7(2018):113–122. https://doi.org/10.3233/978-1-61499-898-3-113 CrossRef

67.

Salem B, Stjepandić J, Stobrawa S (2019) Assessment of methods for industrial indoor object recognition. Adv Transdisciplinary Eng 10:390–399. https://doi.org/10.3233/ATDE190145 CrossRef

68.

Denkena B, Stobrawa S, Sommer M, Stjepandic J, Von Soden M (2020) Production planning using the digital twin | [Effiziente Erstellung eines digitalen Zwillings zur Verwendung im Produktionsmanagement; Produktionsplanung mit dem digitalen Zwilling]. WT Werkstattstechnik 110(10):661–665 CrossRef

69.

Dai S, Zhao G, Yu Y, Bao Q (2020) The evolution from digital mock-up to Digital Twin. Front Artif Intell Appl Mach Learn Artif Intell 332:184–191. https://doi.org/10.3233/FAIA200781

70.

Riascos R, Levy L, Stjepandić J, Fröhlich A (2015) Digital mock-up. In: Stjepandic J et al. (eds) Concurrent engineering in the 21st century: foundations, developments and challenges, Springer International Switzerland, pp 355–388

71.

Schleich B, Dittrich MA, Clausmeyer T, Damgrave R, Erkoyuncu JA, Haefner B, de Lange J, Plakhotnik D, Scheidel W, Wuest T (2019) Shifting value stream patterns along the product lifecycle with digital twins. Procedia CIRP 86:3–11. https://doi.org/10.1016/j.procir.2020.01.049 CrossRef

72.

Redelinghuys AJH, Basson AH, Kruger K (2019) A six-layer architecture for the digital twin: a manufacturing case study implementation. J Intell Manuf. https://doi.org/10.1007/s10845-019-01516-6 CrossRef

73.

Hong Lim KY, Zheng P, Chen CH (2019) A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives. J Intell Manuf. https://doi.org/10.1007/s10845-019-01512-w CrossRef

74.

Polini W, Corrado A (2021) Digital twin of stone sawing processes. Int J Adv Manuf Technol 112:121–131. https://doi.org/10.1007/s00170-020-06384-6 CrossRef

75.

Zhong RY, Ge W (2018) Internet of things enabled manufacturing: a review. Int J Agile Syst Manage 11(2):126–154. https://doi.org/10.1504/IJASM.2018.092545 CrossRef

76.

Guo H, Zhu Y, Zhang Y, Ren Y, Chen M, Zhang R (2021) A digital twin-based layout optimization method for discrete manufacturing workshop. Int J Adv Manuf Technol 112:1307–1318. https://doi.org/10.1007/s00170-020-06568-0 CrossRef

77.

Kritzinger W, Karner M, Traar G, Henjes J, Sihn W (2018) Digital Twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine 51(11):1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474 CrossRef

78.

Otero R, Lagüel S, Garrido I, Arias P (2020) Mobile indoor mapping technologies: a review. Autom Construct 120:103399. https://doi.org/10.1016/j.autcon.2020.103399

Titel: Digital Twin: A Conceptual View
verfasst von: Josip Stjepandić
Markus Sommer
Sebastian Stobrawa
Verlag: Springer International Publishing
Buch: DigiTwin: An Approach for Production Process Optimization in a Built Environment
Print ISBN: 978-3-030-77538-4

Electronic ISBN: 978-3-030-77539-1

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-77539-1_3

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