Digital Twins (DTs) can be constructed for many different applications, leading to substantial differences between different case studies. To be able to learn from the challenges and lessons learned by other DT practitioners, it is important that experience reports be consistent to facilitate comparisons. In this paper, we merge three reference description frameworks for DTs, one generated from a systematic mapping study, one generated from an analysis of experience reports, and one from a systematic literature review, to come up with a unified characterization of DT applications. This analysis has identified six non-overlapping and three cross-cutting characteristics in the reference frameworks. This paper showcases the unified characterization with 21 characteristics to report on a DT case study called the Flex-cell, a manufacturing cell with two robotic arms used for cooperative assembly. The generalizability of this unified characterization is validated using a multi-case approach with another case study in robotics and another in the food industry. We call on the DT community to integrate these systematic reporting principles in their future DT experience reports such that other practitioners can learn from each other more effectively.
KritzingerWKarnerMTraarG, et al. Digital Twin in manufacturing: a categorical literature review and classification. IFAC2018; 51: 1016–1022.
2.
JiTHuangHXuX.Digital Twin Technology—a bibliometric study of top research articles based on Local Citation Score. J Manuf Syst2022; 64: 390–408.
3.
BarbieriGBertuzziACapriottiA, et al. A virtual commissioning based methodology to integrate digital twins into manufacturing systems. Prod Eng2021; 15: 397–412.
4.
Ribeiro da SilvaEAssad NetoANielsenCP.Digital twins: making it feasible for SMEs. In: MadsenOBergerUMøllerC, et al. (eds) The future of smart production for SMEs: a methodological and practical approach towards digitalization in SMEs. Cham: Springer, 2023, pp. 343–348.
5.
XuWCuiJLiL, et al. Digital twin-based industrial cloud robotics: framework, control approach and implementation. J Manuf Syst2021; 58: 196–209.
6.
TaoFXiaoBQiQ, et al. Digital twin modeling. J Manuf Syst2022; 64: 372–389.
7.
OakesBParsaiAVan MierloS, et al. Improving digital twin experience reports. In: Proceedings of the 9th international conference on model-driven engineering and software development, Virtual Event, 8–10 February 2021, pp. 179–190. Setúbal: Science and Technology Publications.
8.
GilSMikkelsenPHTolaD, et al. A modeling approach for composed digital twins in cooperative systems. In: 2023 IEEE 28th International conference on emerging technologies and factory automation (ETFA), Sinaia, 12–15 September 2023, pp. 1–8. New York: IEEE.
9.
MalikAABremA.Digital twins for collaborative robots: a case study in human-robot interaction. Robot Comput Integr Manuf2021; 68: 102092.
10.
MazumderASahedMTasneemZ, et al. Towards next generation digital twin in robotics: trends, scopes, challenges, and future. Heliyon2023; 9: e13359.
11.
HeithoffMKonersmannMMichaelJ, et al. Challenges of integrating model-based digital twins for vehicle diagnosis. In: 2023 ACM/IEEE international conference on model driven engineering languages and systems companion (MODELS-C), Piscataway, NJ, 1–6 October 2023, pp. 470–478. New York: IEEE.
12.
MichaelJPfeifferJRumpeB, et al. Integration challenges for digital twin systems-of-systems. In: 2022 IEEE/ACM 10th international workshop on software engineering for systems-of-systems and software ecosystems (SESoS), Pittsburgh, PA, 16 May 2022, pp. 9–12. New York: IEEE.
13.
VanDerHornEMahadevanS.Digital Twin: generalization, characterization and implementation. Decis Support Syst2021; 145: 113524.
14.
DaliborMJansenNRumpeB, et al. A cross-domain systematic mapping study on software engineering for digital twins. J Syst Softw2022; 193: 111361.
15.
International Organization for Standardization. Automation systems and integration—digital twin framework for manufacturing (ISO 23247:2021). Geneva: International Organization for Standardization, 2021, https://www.iso.org/standard/78743.html
16.
IEC. Asset Administration Shell for industrial applications—Part 1: Asset Administration Shell structure (IEC 63278-1:2023). Geneva: International Electrotechnical Commission, 2023, https://webstore.iec.ch/publication/65628
17.
GilSMikkelsenPHGomesC, et al. Survey on open-source digital twin frameworks–a case study approach. Software2024; 54: 929–960.
18.
JonesDSniderCNassehiA, et al. Characterising the Digital Twin: a systematic literature review. CIRP J Manuf Sci Technol2020; 29: 36–52.
19.
TalasilaPGomesCMikkelsenPH, et al. Digital Twin as a Service (DTaaS): a platform for digital twin developers and users. In: IEEE smart world congress (SWC), Portsmouth, United Kingdom, 28–31 August 2023, pp. 1–8. Portsmouth: IEEE.
LehnerDPfeifferJTinselEF, et al. Digital Twin platforms: requirements, capabilities, and future prospects. IEEE Softw2022; 39: 53–61.
22.
SchluseMPriggemeyerMAtorfL, et al. Experimentable Digital Twins-streamlining simulation-based systems engineering for industry 4.0. IEEE Trans Ind Inform2018; 14: 1722–1731.
23.
GomesCThuleCBromanD, et al. Co-simulation: a survey. ACM Comput Surv2018; 51: 49.
24.
FrasheriMEjersboHThuleC, et al. Rmqfmu: bridging the real world with co-simulation for practitioners. In: MacedoHDThuleCPierceK (eds) Proceedings of the 19th international overture workshop. Ithaca, NY: Cornell University, pp. 66–80.
TalasilaPCrăciuneanDCBogdan-ConstantinP, et al. Comparison between the HUBCAP and DIGITBrain platforms for model-based design and evaluation of Digital Twins. In: CeroneAAutiliMBucaioniA (eds) Lecture notes in computer science (Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Cham: Springer, pp. 238–244.
28.
HumanCBassonAHKrugerK.A design framework for a system of digital twins and services. Comput Ind2023; 144: 103796.
29.
TolaDMadsenEGomesC, et al. Towards easy robot system integration: challenges and future directions. In: 2022 IEEE/SICE international symposium on system integration (SII), Narvik, 9–12 January 2022, pp. 77–82. New York: IEEE.
30.
GilSSchouCMikkelsenPH, et al. Integrating skills into digital twins in cooperative systems. In: 2024 IEEE/SICE international symposium on system integration (SII), Ha Long, Vietnam, 8–11 January 2024, pp. 1124–1131. New York: IEEE.
31.
LehnerDGilSMikkelsenPH, et al. An architectural extension for digital twin platforms to leverage behavioral models. In: 2023 IEEE 19th international conference on automation science and engineering (CASE), Auckland, New Zealand, 26–30 August 2023, pp. 1–8. New York: IEEE.
32.
HansenSTGomesCAGNajafiM, et al. The FMI 3.0 standard interface for clocked and scheduled simulations. Electronics2022; 11, 3635.
33.
ThuleCLausdahlKGomesC, et al. Maestro: the INTO-CPS co-simulation framework. Simul Model Pract Theor2019; 92: 45–61.
34.
CorkePHavilandJ. Not your grandmother’s toolbox —the robotics toolbox reinvented for python. In: 2021 IEEE international conference on robotics and automation (ICRA), Xi’an, China, 30 May–5 June 2021, pp. 11357–11363. New York: IEEE.
35.
LegaardCMTolaDSchranzT, et al. A universal mechanism for implementing functional mock-up units. In: 11th international conference on simulation and modeling methodologies, technologies and applications—SIMULTECH, Virtual Event, 7–9July2021, pp. 121–129. Setúbal: Science and Technology Publications.
36.
Satya Durga Manohar SahuVSamalPKumar PanigrahiC. Modelling, and control techniques of robotic manipulators: a review. Mater Today Proc2021; 56: 2758–2766.
37.
DasANabiM. A review on soft robotics: modeling, control and applications in human-robot interaction. In: Proceedings - 2019 international conference on computing, communication, and intelligent systems, ICCCIS 2019, Greater Noida, India, 18–19 October 2019, pp. 306–311. New York: IEEE.
38.
MadsenETolaDHansenC, et al. AURT: a tool for dynamics calibration of robot manipulators. In: 2022 IEEE/SICE international symposium on system integration (SII), Narvik, Norway, 9–12 January 2022, pp. 190–195. New York: IEEE.
39.
AzizkhaniMGodageISChenY.Dynamic control of soft robotic arm: a simulation study. IEEE Robot Autom Lett2022; 7: 3584–3591.
40.
KousiNGkournelosCAivaliotisS, et al. Digital twin for adaptation of robots’ behavior in flexible robotic assembly lines. Procedia Manuf2019; 28: 121–126.
41.
GualtieriLRauchEVidoniR.Methodology for the definition of the optimal assembly cycle and calculation of the optimized assembly cycle time in human-robot collaborative assembly. Int J Adv Manuf Technol2021; 113: 2369–2384.
OakesBJParsaiAMeyersB, et al. A digital twin description framework and its mapping to Asset Administration Shell. In: PiresLFHammoudiSSeidewitzE (eds) Model-driven engineering and software development, communications in computer and information science (Vol. 1708). Cham: Springer, pp. 1–24.
Lumer-KlabbersGHaustedJOKvistgaardJL, et al. Towards a digital twin framework for autonomous robots. In: Proceedings of the 2021 IEEE 45th annual computers, software, and applications conference (COMPSAC 2021), Madrid, 12–16 July 2021, pp. 1254–1259. New York: IEEE.
46.
FengHGomesCThuleC, et al. The incubator case study for digital twin engineering. arXiv:2102.10390.
47.
KakavandiFGomesCReusRD, et al. Towards developing a digital twin for a manufacturing pilot line: an industrial case study. In: KaraarslanEAydinÖCaliÜ, et al. (eds) Digital Twin driven intelligent systems and emerging metaverse. Singapore: Springer Nature, 2023, pp. 39–64.
48.
FoldagerFBallingOGambleC, et al. Design space exploration in the development of agricultural robots. In: Book of abstracts of the European conference on agricultural engineering: AgEng conference, Wageningen, 8–12 July 2018, pp. 60–61. Wageningen: Wageningen University.
49.
FengHGomesCGilS, et al. Integration of the Mape-K loop in digital twins. In: Annual modeling and simulation conference (ANNSIM 2022), San Diego, CA, 18–20 July 2022, pp. 102–113. New York: IEEE.
50.
WrightTGomesCWoodcockJ.Formally verified self-adaptation of an incubator digital twin. In: MargariaTSteffenB (eds) Leveraging applications of formal methods, verification and validation. Cham: Springer Nature, 2022, pp. 89–109.
51.
SteinkrausKHHwaYBVan BurenJ, et al. Studies on tempeh. An Indonesian fermented soybean food. Food Res1960; 25: 777–788.
52.
ZhangHLiGHatledalLI, et al. A digital twin of the research vessel Gunnerus for lifecycle services: outlining key technologies. IEEE Robot Autom Mag2023; 30: 6–19.
53.
Guerra-ZubiagaDKutsVMahmoodK, et al. An approach to develop a digital twin for industry 4.0 systems: manufacturing automation case studies. Int J Comput Integr Manuf2021; 34: 933–949.
54.
DavidIArchambaultPWolakQ, et al. Digital twins for cyber-biophysical systems: challenges and lessons learned. In: 26th ACM/IEEE international conference on model driven engineering languages and systems, MODELS 2023, Västerås, 1–6 October 2023, pp. 1–12. New York: IEEE.
55.
OnajiITiwariDSoulatiantorkP, et al. Digital twin in manufacturing: conceptual framework and case studies. Int J Comput Integr Manuf2022; 35: 831–858.
56.
KundischDMuntermannJOberländerAM, et al. An update for taxonomy designers: methodological guidance from information systems research. Bus Inf Syst Eng2022; 64: 421–439.
57.
YinRK.Case study research: design and methods (Vol. 6). Thousand Oaks, CA: Sage, 2018.
58.
RunesonPHöstM.Guidelines for conducting and reporting case study research in software engineering. Empir Softw Eng2009; 14: 131–164.
59.
OakesBJMeyersBJanssensD, et al. Structuring and accessing knowledge for historical and streaming digital twins. In: First workshop on ontology-driven conceptual modeling of digital twins, CEUR workshop proceedings, Amsterdam, 1–13 September 2021, https://ceur-ws.org/Vol-2941/paper17.pdf
60.
FengHGomesCThuleC, et al. Introduction to digital twin engineering. In: 2021 annual modeling and simulation conference (ANNSIM), Fairfax, VA, 19–22 July 2021, pp. 1–12. New York: IEEE.
61.
LiuMFangSDongH, et al. Review of digital twin about concepts, technologies, and industrial applications. J Manuf Syst2021; 58: 346–361.
62.
OakesBJGomesCLarsenPG, et al. Examining model qualities and their impact on digital twins. In: BlasMJAlvarezG (eds) Annual modeling and simulation conference, ANNSIM 2023, Hamilton, ON, Canada, 23–26 May 2023, pp. 220–232. New York: IEEE, https://ieeexplore.ieee.org/document/10155350
63.
TsangEW.Generalizing from research findings: the merits of case studies. Int J Manage Rev2014; 16: 369–383.
QuigleyMGerkeyBConleyK, et al. ROS: an open-source Robot Operating System. In: ICRA workshop on open source software, Kobe, Japan, 12–17 May 2009.
66.
FrasheriMLumer-KlabbersGKvistgaardJL, et al. Building safer robots: from simulation to hardware deployment. In: 2023 27th international conference on methods and models in automation and robotics (MMAR), Międzyzdroje, 22–25 August 2023, pp. 63–68. New York: IEEE.
67.
EjersboHLausdahlKFrasheriM, et al. Dynamic runtime integration of new models in digital twins. In: 2023 IEEE/ACM 18th symposium on software engineering for adaptive and self-managing systems (SEAMS), Melbourne, VIC, Australia, 15–16 May 2023, pp. 44–55. New York: IEEE.
68.
FoldagerFFThuleCBallingO, et al. Towards a digital twin—modelling an agricultural vehicle. In: Margaria T and Steffen B (eds) Lecture notes in computer science (Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Cham: Springer, 2020, pp. 109–123.
69.
ParedisRGomesCVangheluweH. A family of digital t workflows and architectures: exploring two cases. In: SmirnovAPanettoHMadaniK (eds) Innovative intelligent industrial production and logistics. Cham: Springer, pp. 93–109.
70.
SchouCAndersenRSChrysostomouD, et al. Skill-based instruction of collaborative robots in industrial settings. Robot Comput Integr Manuf2018; 53: 72–80.
71.
CavalcantiABarnettWBaxterJ, et al. Robostar technology: a roboticist’s toolbox for combined proof, simulation, and testing. In: CavalcantiABarnettWBaxterJ, et al. (eds) Software engineering for robotics. Cham: Springer, 2021, pp. 249–293.
