Modeling languages in Industry 4.0: an extended systematic mapping study

Abstract

Industry 4.0 integrates cyber-physical systems with the Internet of Things to optimize the complete value-added chain. Successfully applying Industry 4.0 requires the cooperation of various stakeholders from different domains. Domain-specific modeling languages promise to facilitate their involvement through leveraging (domain-specific) models to primary development artifacts. We aim to assess the use of modeling in Industry 4.0 through the lens of modeling languages in a broad sense. Based on an extensive literature review, we updated our systematic mapping study on modeling languages and modeling techniques used in Industry 4.0  (Wortmann et al., Conference on model-driven engineering languages and systems (MODELS’17), IEEE, pp 281–291, 2017) to include publications until February 2018. Overall, the updated study considers 3344 candidate publications that were systematically investigated until 408 relevant publications were identified. Based on these, we developed an updated map of the research landscape on modeling languages and techniques for Industry 4.0. Research on modeling languages in Industry 4.0 focuses on contributing methods to solve the challenges of digital representation and integration. To this end, languages from systems engineering and knowledge representation are applied most often but rarely combined. There also is a gap between the communities researching and applying modeling languages for Industry 4.0 that originates from different perspectives on modeling and related standards. From the vantage point of modeling, Industry 4.0 is the combination of systems engineering, with cyber-physical systems, and knowledge engineering. Research currently is splintered along topics and communities and accelerating progress demands for multi-disciplinary, integrated research efforts.

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Notes

  1. 1.

    Available from companion website http://gemoc.org/modeling4Industry4.0/.

  2. 2.

    Publish or Perish: https://harzing.com/resources/publish-or-perish.

References

  1. 1.

    Abramovici, M.: Future trends in product lifecycle management (PLM). In: The Future of Product Development, pp. 665–674. Springer, Berlin (2007)

  2. 2.

    Affonso, R.C., Cheutet, V., Ayadi, M., Haddar, M.: Simulation in product lifecycle: towards a better information management for design projects. J. Mod. Project Manag. 1(1) (2013)

  3. 3.

    Agner, L.T.W., Soares, I.W., Stadzisz, P.C., SimãO, J.M.: A Brazilian survey on UML and model-driven practices for embedded software development. J. Syst. Softw. 86(4), 997–1005 (2013)

    Google Scholar 

  4. 4.

    Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-Oriented Data. Human–Computer Interaction Series. Springer, Berlin (2011)

    Google Scholar 

  5. 5.

    Al-Fedaghi, S., Al-Shahin, F.: Control software modeling in production systems. Open Autom. Control Syst. J. 7(1), 184–198 (2015)

    Google Scholar 

  6. 6.

    Aleksić, D.S., Janković, D.S., Stoimenov, L.V.: A case study on the object-oriented framework for modeling product families with the dominant variation of the topology in the one-of-a-kind production. Int. J. Adv. Manuf. Technol. 59(1), 397–412 (2012)

    Google Scholar 

  7. 7.

    Alenazi, M., Niu, N., Wang, W., Gupta, A.: Traceability for automated production systems: a position paper. In: 2017 IEEE 25th International Requirements Engineering Conference Workshops (REW), pp. 51–55. IEEE (2017)

  8. 8.

    Alexopoulos, K., Makris, S., Xanthakis, V., Sipsas, K., Chryssolouris, G.: A concept for context-aware computing in manufacturing: the white goods case. Int. J. Comput. Integr. Manuf. 29(8), 839–849 (2016)

    Google Scholar 

  9. 9.

    Back, M.G., Lee, D.K., Shin, J.G., Woo, J.H.: A study for production simulation model generation system based on data model at a shipyard. Int. J. Naval Archit. Ocean Eng. 8(5), 496–510 (2016)

    Google Scholar 

  10. 10.

    Backhaus, J., Reinhart, G.: Digital description of products, processes and resources for task-oriented programming of assembly systems. J. Intell. Manuf. 28(8), 1787–1800 (2017)

    Google Scholar 

  11. 11.

    Bailey, J., Budgen, D., Turner, M., Kitchenham, B., Brereton, P., Linkman, S.: Evidence relating to object-oriented software design: a survey. In: Proceedings of the First International Symposium on Empirical Software Engineering and Measurement, ESEM ’07, pp. 482–484. IEEE Computer Society, Washington, DC, USA (2007)

  12. 12.

    Bareiß, P., Schütz, D., Priego, R., Marcos, M., Vogel-Heuser, B.: A model-based failure recovery approach for automated production systems combining sysml and industrial standards. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–7. IEEE (2016)

  13. 13.

    Beecham, S., Baddoo, N., Hall, T., Robinson, H., Sharp, H.: Motivation in Software Engineering: a systematic literature review. Inf. Softw. Technol. 50(9–10), 860–878 (2008)

    Google Scholar 

  14. 14.

    Berardinelli, L., Biffl, S., Lüder, A., Mätzler, E., Mayerhofer, T., Wimmer, M., Wolny, S.: Cross-disciplinary engineering with AutomationML and SysML. at-Automatisierungstechnik 64(4), 253–269 (2016)

    Google Scholar 

  15. 15.

    Berardinelli, L., Drath, R., Maetzler, E., Wimmer, M.: On the evolution of CAEX: a language engineering perspective. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8. IEEE (2016)

  16. 16.

    Bergert, M., Diedrich, C., Kiefer, J., Bar, T.: Automated PLC software generation based on standardized digital process information. In: IEEE Conference on Emerging Technologies and Factory Automation. ETFA, pp. 352–359. IEEE (2007)

  17. 17.

    Bergmann, S., Strassburger, S.: Challenges for the automatic generation of simulation models for production systems. In: Proceedings of the 2010 Summer Computer Simulation Conference, SCSC ’10, pp. 545–549. Society for Computer Simulation International, San Diego, CA, USA (2010)

  18. 18.

    Bergmann, S., Straßburger, S.: On the use of the Core Manufacturing Simulation Data (CMSD) standard: experiences and recommendations. In: Fall Simulation Interoperability Workshop 2015 (SIW) (2015)

  19. 19.

    Berndt, O., von Lukas, U.F., Kuijper, A.: Functional modelling and simulation of overall system ship-virtual methods for engineering and commissioning in shipbuilding. In: ECMS, pp. 347–353 (2015)

  20. 20.

    Bigvand, P.G., Drath, R., Scholz, A., Schüller, A.: Agile standardization by means of PCE Requests. In: 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), pp. 1–8. IEEE (2015)

  21. 21.

    Boeker, M., Vach, W., Motschall, E.: Google Scholar as replacement for systematic literature searches: good relative recall and precision are not enough. BMC Med. Res. Methodol. 13(1), 131 (2013)

    Google Scholar 

  22. 22.

    Broy, M., Schmidt, A.: Challenges in engineering cyber-physical systems. Computer 47(2), 70–72 (2014)

    Google Scholar 

  23. 23.

    Bscher, C., Voet, H., Krunke, M., Burggrf, P., Meisen, T., Jeschke, S.: Semantic information modelling for factory planning projects. Procedia CIRP 41, 478–483 (2016)

    Google Scholar 

  24. 24.

    Budgen, D., Brereton, P.: Performing systematic literature reviews in software engineering. In: Proceedings of the 28th International Conference on Software Engineering, pp. 1051–1052. ACM (2006)

  25. 25.

    Budgen, D., Burn, A.J., Brereton, O.P., Kitchenham, B.A., Pretorius, R.: Empirical evidence about the UML: a systematic literature review. Softw. Pract. Exp. 41(4), 363–392 (2011)

    Google Scholar 

  26. 26.

    Budgen, D., Turner, M., Brereton, P., Kitchenham, B.: Using mapping studies in software engineering. In: Proceedings of PPIG, vol. 8, pp. 195–204. Lancaster University (2008)

  27. 27.

    Bundesministerium für Bildung und Forschung: Zukunftsprojekt Industrie 4.0. https://www.bmbf.de/de/zukunftsprojekt-industrie-4-0-848.html. Accessed 20 Apr 2017

  28. 28.

    Cândea, G., Cândea, C., Radu, C., Terkaj, W., Sacco, M., Suciu, O.: A practical use of the Virtual Factory Framework. In: 14th International Conference on Modern Information Technology in the Innovation Process of the Industrial Enterprises, Budapest, Hungary (2012)

  29. 29.

    Chavarra-Barrientos, D., Batres, R., Wright, P.K., Molina, A.: A methodology to create a sensing, smart and sustainable manufacturing enterprise. Int. J. Prod. Res. 56(1–2), 584–603 (2018)

    Google Scholar 

  30. 30.

    Chen, D., Maffei, A., Ferreirar, J., Akillioglu, H., Khabazzi, M.R., Zhang, X.: A virtual environment for the management and development of cyber-physical manufacturing systems. IFAC-PapersOnLine 48(7), 29–36 (2015)

    Google Scholar 

  31. 31.

    Chen, D., Panfilenko, D.V., Khabbazi, M.R., Sonntag, D.: A model-based approach to qualified process automation for anomaly detection and treatment. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8. IEEE (2016)

  32. 32.

    Chen, L., Ali Babar, M., Ali, N.: Variability management in software product lines: a systematic review. In: Proceedings of the 13th International Software Product Line Conference, pp. 81–90. Carnegie Mellon University (2009)

  33. 33.

    Condori-Fernandez, N., Daneva, M., Sikkel, K., Wieringa, R., Dieste, O., Pastor, O.: A systematic mapping study on empirical evaluation of software requirements specifications techniques. In: Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement (2009)

  34. 34.

    Constantinescu, C., Matarazzo, D., Dienes, D., Francalanza, E., Bayer, M.: Modeling of system knowledge for efficient agile manufacturing: tool evaluation, selection and implementation scenario in SMEs. Procedia CIRP 25, 246–252 (2014). 8th International Conference on Digital Enterprise Technology—DET 2014 Disruptive Innovation in Manufacturing Engineering towards the 4th Industrial Revolution

    Google Scholar 

  35. 35.

    Constantinescu, C., Matarazzo, D., Dienes, D., Francalanza, E., Bayer, M.: Modeling of system knowledge for efficient agile manufacturing: tool evaluation, selection and implementation scenario in SMEs. Procedia CIRP 25, 246–252 (2014)

    Google Scholar 

  36. 36.

    Deane, P.M.: The First Industrial Revolution. Cambridge University Press, Cambridge (1979)

    Google Scholar 

  37. 37.

    Dias Neto, A.C., Subramanyan, R., Vieira, M., Travassos, G.H.: A survey on model-based testing approaches: a systematic review. In: Proceedings of the 1st ACM International Workshop on Empirical Assessment of Software Engineering Languages and Technologies: Held in Conjunction with the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 31–36. ACM (2007)

  38. 38.

    Díaz-Madroñero, M., Mula, J., Peidro, D.: A mathematical programming model for integrating production and procurement transport decisions. Appl. Math. Model. 52, 527–543 (2017)

    MathSciNet  Google Scholar 

  39. 39.

    Divoux, T., Rondeau, E., Lepage, F.: Using the EXPRESS language as a reference interface to define MMS communication. J. Intell. Manuf. 8(1), 59–66 (1997)

    Google Scholar 

  40. 40.

    Dorofeev, K., Cheng, C.H., Guedes, M., Ferreira, P., Profanter, S., Zoitl, A.: Device adapter concept towards enabling plug&produce production environments. In: 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8. IEEE (2017)

  41. 41.

    Drath, R., Luder, A., Peschke, J., Hundt, L.: AutomationML—the glue for seamless automation engineering. In: IEEE International Conference on Emerging Technologies and Factory Automation. ETFA 2008, pp. 616–623. IEEE (2008)

  42. 42.

    Dregger, J., Niehaus, J., Ittermann, P., Hirsch-Kreinsen, H., ten Hompel, M.: The digitization of manufacturing and its societal challenges: a framework for the future of industrial labor. In: 2016 IEEE International Symposium on Ethics in Engineering, Science and Technology (ETHICS), pp. 1–3 (2016)

  43. 43.

    Du, J., He, Q., Fan, X.: Automating generation of the assembly line models in aircraft manufacturing simulation. In: 2013 IEEE International Symposium on Assembly and Manufacturing (ISAM), , pp. 155–159. IEEE (2013)

  44. 44.

    do Nascimento, L.M., Viana, D.L., Neto, P.A.S., Martins, D.A., Garcia, V.C., Meira, S.R.: A systematic mapping study on domain-specific languages. In: The Seventh International Conference on Software Engineering Advances (ICSEA 2012), pp. 179–187 (2012)

  45. 45.

    Efendioglu, N., Woitsch, R.: A modelling method for digital service design and intellectual property management towards Industry 4.0: CAxMan case. In: International Conference on Serviceology, pp. 153–163. Springer, Berlin (2017)

    Google Scholar 

  46. 46.

    Engström, E., Runeson, P.: Software product line testing—a systematic mapping study. Inf. Softw. Technol. 53(1), 2–13 (2011)

    Google Scholar 

  47. 47.

    Engström, E., Runeson, P.: Software product line testing-a systematic mapping study. Inf. Softw. Technol. 53(1), 2–13 (2011)

    Google Scholar 

  48. 48.

    Feldmann, S., Herzig, S.J., Kernschmidt, K., Wolfenstetter, T., Kammerl, D., Qamar, A., Lindemann, U., Krcmar, H., Paredis, C.J., Vogel-Heuser, B.: Towards effective management of inconsistencies in model-based engineering of automated production systems. IFAC-PapersOnLine 48(3), 916–923 (2015)

    Google Scholar 

  49. 49.

    Foradis, T., Thramboulidis, K.: From mechatronic components to industrial automation things—an IoT model for cyber-physical manufacturing systems. J. Softw. Eng. Appl. 10(08), 734 (2017)

    Google Scholar 

  50. 50.

    Francalanza, E., Borg, J., Constantinescu, C.: A knowledge-based tool for designing cyber physical production systems. Comput. Ind. 84, 39–58 (2017)

    Google Scholar 

  51. 51.

    García, J., Cabot, J.: Stepwise adoption of continuous delivery in model-driven engineering. In: DEVOPS (2018)

  52. 52.

    García, M.V., Irisarri, E., Pérez, F., Estévez, E., Marcos, M.: OPC-UA communications integration using a CPPS architecture. In: Ecuador Technical Chapters Meeting (ETCM), IEEE, vol. 1, pp. 1–6. IEEE (2016)

  53. 53.

    García-Borgoñon, L., Barcelona, M., García-García, J., Alba, M., Escalona, M.J.: Software process modeling languages: a systematic literature review. Inf. Softw. Technol. 56(2), 103–116 (2014)

    Google Scholar 

  54. 54.

    Gisbert, J.R., Palau, C., Uriarte, M., Prieto, G., Palazón, J.A., Esteve, M., López, O., Correas, J., Lucas-Estañ, M.C., Giménez, P., et al.: Integrated system for control and monitoring industrial wireless networks for labor risk prevention. J. Netw. Comput. Appl. 39, 233–252 (2014)

    Google Scholar 

  55. 55.

    Givehchi, O., Landsdorf, K., Simoens, P., Colombo, A.W.: Interoperability for industrial cyber-physical systems: an approach for legacy systems. IEEE Trans. Ind. Inform. 13(6), 3370–3378 (2017)

    Google Scholar 

  56. 56.

    Göring, M., Fay, A.: Automation systems—formal modeling of temporal change of physical structure. In: IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society, pp. 6160–6165. IEEE (2012)

  57. 57.

    Gregor, M., Hromada, J., Matuszek, J.: Digital Factory supported by simulation and metamodelling. Appl. Comput. Sci. 4, 63–74 (2008)

    Google Scholar 

  58. 58.

    Gruhn, V., Schäfer, C.: BizDevOps: because DevOps is not the end of the story. In: International Conference on Intelligent Software Methodologies, Tools, and Techniques, pp. 388–398. Springer, Berlin (2015)

    Google Scholar 

  59. 59.

    Hall, T., Beecham, S., Bowes, D., Gray, D., Counsell, S.: A systematic literature review on fault prediction performance in software engineering. IEEE Trans. Softw. Eng. 38(6), 1276–1304 (2012)

    Google Scholar 

  60. 60.

    Harcuba, O., Vrba, P.: Ontologies for flexible production systems. In: 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), pp. 1–8. IEEE (2015)

  61. 61.

    Hasan, B., Wikander, J.: A review on utilizing ontological approaches in integrating assembly design and assembly process planning. Int. J. Mech. Eng. (SSRG-IJME) 4(11), 5–16 (2017)

    Google Scholar 

  62. 62.

    Heineck, T., Gonçalves, E., Sousa, A., Oliveira, M., Castro, J.: Model-driven development in robotics domain: a systematic literature review. In: 2016 X Brazilian Symposium on Software Components, Architectures and Reuse (SBCARS), pp. 151–160. IEEE (2016)

  63. 63.

    Hermann, M., Pentek, T., Otto, B.: Design principles for Industrie 4.0 scenarios. In: 2016 49th Hawaii International Conference on System Sciences (HICSS), pp. 3928–3937. IEEE (2016)

  64. 64.

    Hermann, M., Pentek, T., Otto, B.: Design principles for Industrie 4.0 scenarios. In: Proceedings of the 2016 49th Hawaii International Conference on System Sciences (HICSS), HICSS ’16, pp. 3928–3937. IEEE Computer Society, Washington, DC, USA (2016)

  65. 65.

    High Value Manufacturing Carapult. https://hvm.catapult.org.uk/. Accessed 5 June 2018

  66. 66.

    Hildebrandt, C., Glawe, M., Müller, A.W., Fay, A.: Reasoning on engineering knowledge: applications and desired features. In: European Semantic Web Conference, pp. 65–78. Springer, Berlin (2017)

    Google Scholar 

  67. 67.

    Hoffmann, A., Angerer, A., Schierl, A., Vistein, M., Reif, W.: Service-oriented robotics manufacturing by reasoning about the scene graph of a robotics cell. In: Proceedings of ISR/Robotik 2014; 41st International Symposium on Robotics, pp. 1–8. VDE (2014)

  68. 68.

    Holz, D., Topalidou-Kyniazopoulou, A., Rovida, F., Pedersen, M.R., Krüger, V., Behnke, S.: A skill-based system for object perception and manipulation for automating kitting tasks. In: 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), , pp. 1–9. IEEE (2015)

  69. 69.

    Hummel, B., Braun, P.: Towards an integrated system model for testing and verification of automation machines. In: Proceedings of the 2008 International Workshop on Models in Software Engineering, MiSE ’08, pp. 51–56. ACM, New York, NY, USA (2008)

  70. 70.

    Irisarri, E., García, M.V., Pérez, F., Estévez, E., Marcos, M.: A model-based approach for process monitoring in oil production industry. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–4. IEEE (2016)

  71. 71.

    Jorgensen, M., Shepperd, M.: A systematic review of software development cost estimation studies. IEEE Trans. Softw. Eng. 33(1), 33–53 (2007)

    Google Scholar 

  72. 72.

    Jung, K., Kulvatunyou, B., Choi, S., Brundage, M.P.: An overview of a smart manufacturing system readiness assessment. In: IFIP International Conference on Advances in Production Management Systems, pp. 705–712. Springer, Berlin (2016)

    Google Scholar 

  73. 73.

    Juristo, N., Moreno, A.M., Vegas, S., Solari, M.: In search of what we experimentally know about unit testing. IEEE Softw. 23(6), 72–80 (2006)

    Google Scholar 

  74. 74.

    Kádár, B., Terkaj, W., Sacco, M.: Semantic Virtual Factory supporting interoperable modelling and evaluation of production systems. CIRP Ann. Manuf. Technol. 62(1), 443–446 (2013)

    Google Scholar 

  75. 75.

    Kannengiesser, U., Müller, H.: Towards agent-based smart factories: a subject-oriented modeling approach. In: 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 3, pp. 83–86. IEEE (2013)

  76. 76.

    Keele, S.: Guidelines for performing systematic literature reviews in software engineering, vol. 5. Technical report, Ver. 2.3 EBSE Technical Report (2007)

  77. 77.

    Kern, H., Stefan, F., Dimitrieski, V.: Intelligent and self-adapting integration between machines and information systems. IADIS Int. J. Comput. Sci. Inf. Syst. 10(1), 47–63 (2015)

  78. 78.

    Khaleel, H., Conzon, D., Kasinathan, P., Brizzi, P., Pastrone, C., Pramudianto, F., Eisenhauer, M., Cultrona, P.A., Rusinà, F., Lukáč, G., et al.: Heterogeneous applications, tools, and methodologies in the car manufacturing industry through an IoT approach. IEEE Syst. J. 11(3), 1412–1423 (2017)

    Google Scholar 

  79. 79.

    Khan, A., Turowski, K.: A survey of current challenges in manufacturing industry and preparation for Industry 4.0. In: Proceedings of the First International Scientific Conference on Intelligent Information Technologies for Industry (IITI’16), pp. 15–26 (2016). https://doi.org/10.1007/978-3-319-33609-1_2

    Google Scholar 

  80. 80.

    Kitchenham, B., Brereton, O.P., Budgen, D., Turner, M., Bailey, J., Linkman, S.: Systematic literature reviews in software engineering—a systematic literature review. Inf. Softw. Technol. 51(1), 7–15 (2009). Special Section—Most Cited Articles in 2002 and Regular Research Papers

    Google Scholar 

  81. 81.

    Kitchenham, B.A., Budgen, D., Brereton, O.P.: The value of mapping studies: a participant-observer case study. In: Proceedings of the 14th International Conference on Evaluation and Assessment in Software Engineering, EASE’10, pp. 25–33. BCS Learning & Development Ltd., Swindon, UK (2010)

  82. 82.

    Kosar, T., Bohra, S., Mernik, M.: Domain-specific languages: a systematic mapping study. Inf. Softw. Technol. 71, 77–91 (2016)

    Google Scholar 

  83. 83.

    Kovalenko, O., Wimmer, M., Sabou, M., Lüder, A., Ekaputra, F.J., Biffl, S.: Modeling AutomationML: semantic web technologies vs. model-driven engineering. In: 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), pp. 1–4. IEEE (2015)

  84. 84.

    Korea-Manufacturing Technology-Smart Factory. https://www.export.gov/article?id=Korea-Manufacturing-Technology-Smart-Factory. Accessed 4 June 2018

  85. 85.

    Laguna, M.A., Crespo, Y.: A systematic mapping study on software product line evolution: from legacy system reengineering to product line refactoring. Sci. Comput. Program. 78(8), 1010–1034 (2013)

    Google Scholar 

  86. 86.

    Lahire, P., Parigot, D., Tundrea, E.: SMARTFACTORY—an implementation of the domain driven development approach. In: SACI2004, 1st Romanian-Hungarian Joint Symposium on Applied Computational Intelligence, p. 6 (2004)

  87. 87.

    Lee, E.A.: Cyber physical systems: design challenges. In: Proceedings of the 11th IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC), pp. 363–369 (2008)

  88. 88.

    Liao, Y., Deschamps, F., de Freitas Rocha Loures, E., Ramos, L.F.P.: Past, present and future of Industry 4.0—a systematic literature review and research agenda proposal. Int. J. Prod. Res. 55(12), 3609–3629 (2017). https://doi.org/10.1080/00207543.2017.1308576

    Article  Google Scholar 

  89. 89.

    Liebel, G., Marko, N., Tichy, M., Leitner, A., Hansson, J.: Assessing the state-of-practice of model-based engineering in the embedded systems domain. In: International Conference on Model Driven Engineering Languages and Systems, pp. 166–182. Springer, Berlin (2014)

    Google Scholar 

  90. 90.

    Long, F., Zeiler, P., Bertsche, B.: Potentials of coloured petri nets for realistic availability modelling of production systems in Industry 4.0. In: Proceedings of the ESREL 2015 Conference, 07.09.-10.09. 2015, Zürich, Switzerland, pp. 4455–4463 (2015)

  91. 91.

    Loskyll, M., Heck, I., Schlick, J., Schwarz, M.: Context-based orchestration for control of resource-efficient manufacturing processes. Future Internet 4(3), 737–761 (2012)

    Google Scholar 

  92. 92.

    Lu, Y.: Industry 4.0: a survey on technologies, applications and open research issues. J. Ind. Inf. Integr. 6, 1–10 (2017)

    Google Scholar 

  93. 93.

    Lütjen, M., Kreowski, H.J., Franke, M., Thoben, K.D., Freitag, M.: Model-driven logistics engineering-challenges of model and object transformation. Procedia Technol. 15, 303–312 (2014)

    Google Scholar 

  94. 94.

    Lütjen, M., Rippel, D.: GRAMOSA framework for graphical modelling and simulation-based analysis of complex production processes. Int. J. Adv. Manuf. Technol. 81(1–4), 171–181 (2015)

    Google Scholar 

  95. 95.

    Ma, Z., Hudic, A., Shaaban, A., Plosz, S.: Security viewpoint in a reference architecture model for cyber-physical production systems. In: 2017 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), pp. 153–159. IEEE (2017)

  96. 96.

    Made in China 2025. https://www.merics.org/sites/default/files/2017-09/MPOC_No.2_MadeinChina2025.pdf. Accessed 6 June 2018

  97. 97.

    Mahdavi-Hezavehi, S., Durelli, V.H., Weyns, D., Avgeriou, P.: A systematic literature review on methods that handle multiple quality attributes in architecture-based self-adaptive systems. Inf. Softw. Technol. 90, 1–26 (2017)

    Google Scholar 

  98. 98.

    Matei, M.M., Popescu, D.: Extend IT services in process control domain for onshore oilfields. In: 10th International Conference on Dynamical Systems and Control (CONTROL15), December, pp. 12–14 (2015)

  99. 99.

    Mätzler, S., Wollschlaeger, M.: Interchange format for the generation of functional elements for industrie 4.0 components. In: Industrial Electronics Society, IECON 2017-43rd Annual Conference of the IEEE, pp. 5453–5459. IEEE (2017)

  100. 100.

    Mazak, A., Huemer, C.: A standards framework for value networks in the context of Industry 4.0. In: 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 1342–1346. IEEE (2015)

  101. 101.

    McMillan, A.J., Swindells, N., Archer, E., McIlhagger, A., Sung, A., Leong, K., Jones, R.: A review of composite product data interoperability and product life-cycle management challenges in the composites industry. Adv. Manuf. Polym. Compos. Sci. 3(4), 130–147 (2017)

    Google Scholar 

  102. 102.

    Mechs, S., Grimm, S., Beyer, D., Lamparter, S.: Evaluation of prediction accuracy for energy-efficient switching of automation facilities. In: Industrial Electronics Society, IECON 2013-39th Annual Conference of the IEEE, pp. 6928–6933. IEEE (2013)

  103. 103.

    Medvidovic, N., Taylor, R.N.: A classification and comparison framework for software architecture description languages. IEEE Trans. Softw. Eng. 26, 70–93 (2000)

    Google Scholar 

  104. 104.

    Mehmood, A., Jawawi, D.N.: Aspect-oriented model-driven code generation: a systematic mapping study. Inf. Softw. Technol. 55(2), 395–411 (2013). Special Section: Component-Based Software Engineering (CBSE) (2011)

  105. 105.

    Merkumians, M.M., Baierling, M., Schitter, G.: A service-oriented domain specific language programming approach for batch processes. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–9. IEEE (2016)

  106. 106.

    Michaloski, J., Proctor, F., Arinez, J., Berglund, J.: Toward the ideal of automating production optimization. In: ASME 2013 International Mechanical Engineering Congress and Exposition, p. V02AT02A089. American Society of Mechanical Engineers (2013)

  107. 107.

    Miguel Gutierrez-Guerrero, J., Antonio Holgado-Terriza, J.: iMMAS an industrial meta-model for automation system using OPC UA. Elektronika ir Elektrotechnika 23(3), 3–11 (2017)

    Google Scholar 

  108. 108.

    Mokyr, J.: The second industrial revolution, 1870–1914. Storia delleconomia Mondiale, pp. 219–45 (1998)

  109. 109.

    Mosterman, P.J., Zander, J.: Cyber-physical systems challenges: a needs analysis for collaborating embedded software systems. Softw. Syst. Model. 15(1), 5–16 (2016)

    Google Scholar 

  110. 110.

    Negri, E., Fumagalli, L., Garetti, M., Tanca, L.: Requirements and languages for the semantic representation of manufacturing systems. Comput. Ind. 81, 55–66 (2016)

    Google Scholar 

  111. 111.

    Negri, E., Perotti, S., Fumagalli, L., Marchet, G., Garetti, M.: Modelling internal logistics systems through ontologies. Comput. Ind. 88, 19–34 (2017)

    Google Scholar 

  112. 112.

    Niggemann, O., Maier, A., Jasperneite, J.: Model-based development of automation systems. In: MBEES, pp. 45–54 (2010)

  113. 113.

    Onori, M., Semere, D., Barata, J.: Evolvable assembly systems: from evaluation to application. In: Innovation in Manufacturing Networks, pp. 205–214. Springer, Berlin (2008)

  114. 114.

    Pedrazzoli, P., Alge, M., Bettoni, A., Canetta, L.: Modeling and simulation tool for sustainable MC supply chain design and assessment. In: IFIP International Conference on Advances in Production Management Systems, pp. 342–349. Springer, Berlin (2012)

    Google Scholar 

  115. 115.

    Petersen, K., Feldt, R., Mujtaba, S., Mattsson, M.: Systematic mapping studies in software engineering. EASE 8, 68–77 (2008)

    Google Scholar 

  116. 116.

    Petrasch, R., Hentschke, R.: Process modeling for Industry 4.0 applications: towards an Industry 4.0 process modeling language and method. In: 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 1–5. IEEE (2016)

  117. 117.

    Pfouga, A., Stjepandić, J.: Leveraging 3D geometric knowledge in the product lifecycle based on industrial standards. J. Comput. Des. Eng. 5, 54–67 (2017)

    Google Scholar 

  118. 118.

    Pisching, M.A., Junqueira, F., Filho, D.J.S., Miyagi, P.E.: Service on the Industry 4.0, pp. 65–72. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16766-4_7

    Google Scholar 

  119. 119.

    Polacsek, T., Roussel, S., Bouissiere, F., Cuiller, C., Dereux, P.E., Kersuzan, S.: Towards thinking manufacturing and design together: an aeronautical case study. In: International Conference on Conceptual Modeling, pp. 340–353. Springer, Berlin (2017)

    Google Scholar 

  120. 120.

    Pratt, M.J.: Introduction to ISO 10303 the STEP standard for product data exchange. J. Comput. Inf. Sci. Eng. 1(1), 102–103 (2001)

    Google Scholar 

  121. 121.

    Pretorius, R., Budgen, D.: A mapping study on empirical evidence related to the models and forms used in the UML. In: Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM ’08, pp. 342–344. ACM, New York, NY, USA (2008)

  122. 122.

    Prévost, G., Blech, J.O., Foster, K., Schmidt, H.W.: An architecture for visualization of industrial automation data. In: ENASE, pp. 38–46 (2017)

  123. 123.

    Priego, R., Agirre, A., Estévez, E., Orive, D., Marcos, M.: Middleware-based support for reconfigurable mechatronic systems. IFAC-PapersOnLine 48(10), 81–86 (2015)

    Google Scholar 

  124. 124.

    Ranky, P.G., Lonkar, M., Chamyvelumani, S.: eTransition models of collaborating design and manufacturing enterprises. Int. J. Comput. Integr. Manuf. 16(4–5), 255–266 (2003)

    Google Scholar 

  125. 125.

    Rashid, M.A., Qureshi, H., Khan, N.: ERP life-cycle management for aerospace smart factory: a multi-disciplinary approach. Int. J. Comput. Appl. 26(11), 55–62 (2011)

  126. 126.

    Ren, G., Hua, Q., Deng, P., Yang, C., Zhang, J.: A multi-perspective method for analysis of cooperative behaviors among industrial devices of smart factory. IEEE Access 5, 10882–10891 (2017)

    Google Scholar 

  127. 127.

    Runde, S., Wolf, G., Braun, M., Siemens, A.: EDDL and semantic web From field device integration (FDI) to Future Device Management (FDM). In: 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA), pp. 1–8. IEEE (2013)

  128. 128.

    Sabou, M., Ekaputra, F., Kovalenko, O., Biffl, S.: Supporting the engineering of cyber-physical production systems with the AutomationML analyzer. In: 2016 1st International Workshop on Cyber-Physical Production Systems (CPPS), pp. 1–8. IEEE (2016)

  129. 129.

    Sadigh, B.L., Unver, H.O., Nikghadam, S., Dogdu, E., Ozbayoglu, A.M., Kilic, S.E.: An ontology-based multi-agent virtual enterprise system (OMAVE): part 1: domain modelling and rule management. Int. J. Comput. Integr. Manuf. 30(2–3), 320–343 (2017)

    Google Scholar 

  130. 130.

    Sadlauer, A., Hehenberger, P.: Using design languages in model-based mechatronic system design processes. Int. J. Agile Syst. Manag. 10(1), 73–91 (2017)

    Google Scholar 

  131. 131.

    Saraeian, S., Shirazi, B., Motameni, H.: Towards an extended BPMS prototype: open challenges of BPM to flexible and robust orchestrate of uncertain processes. Comput. Stand. Interfaces 57, 1–9 (2017)

    Google Scholar 

  132. 132.

    Sarigecili, M.I., Roy, U., Rachuri, S.: Enriching step product model with geometric dimension and tolerance information for one-dimensional tolerance analysis. J. Comput. Inf. Sci. Eng. 17(2), 021004 (2017)

    Google Scholar 

  133. 133.

    Schneider, M., Mittag, T., Gausemeier, J.: Modeling Language for Value Networks. In: 25th International Association for Management of Technology Conference Proceedings, 25th International Association for Management of Technology Conference, vol. 25, pp. 94–110. International Association for Management of Technology (IAMOT), IAMOT, Orlando, Florida (2016)

  134. 134.

    Schubert, D., Heinzemann, C., Gerking, C.: Towards safe execution of reconfigurations in cyber-physical systems. In: 2016 19th International ACM SIGSOFT Symposium on Component-Based Software Engineering (CBSE), pp. 33–38. IEEE (2016)

  135. 135.

    Sjoberg, D.I.K., Hannay, J.E., Hansen, O., Kampenes, V.B., Karahasanovic, A., Liborg, N.K., Rekdal, A.C.: A survey of controlled experiments in software engineering. IEEE Trans. Softw. Eng. 31(9), 733–753 (2005)

    Google Scholar 

  136. 136.

    Soares, A.L., Ferreira, J.P., Mendonça, J.: Organizational behaviour analysis and information technology fitness in manufacturing. In: Balanced Automation Systems, pp. 319–326. Springer, Berlin (1995)

    Google Scholar 

  137. 137.

    Soylu, A., Kharlamov, E., Zheleznyakov, D., Jimenez-Ruiz, E., Giese, M., Skjæveland, M.G., Hovland, D., Schlatte, R., Brandt, S., Lie, H., et al.: Optiquevqs: a visual query system over ontologies for industry. Semantic Web (Preprint) 1–34 (2018)

  138. 138.

    Steimer, C., Fischer, J., Aurich, J.C.: Model-based design process for the early phases of manufacturing system planning using SysML. Procedia CIRP 60, 163–168 (2017)

    Google Scholar 

  139. 139.

    Steinegger, M., Melik-Merkumians, M., Zajc, J., Schitter, G.: A framework for automatic knowledge-based fault detection in industrial conveyor systems. In: 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–6. IEEE (2017)

  140. 140.

    Stemmler, S., Reiter, M., Abel, D.: Model predictive control as a module for autonomously running complex plastics production processes. Int. Polym. Sci. Technol. 41(12), T1 (2014)

    Google Scholar 

  141. 141.

    Strang, D., Anderl, R.: Assembly process driven component data model in cyber-physical production systems. In: Proceedings of the World Congress on Engineering and Computer Science, vol. 2 (2014)

  142. 142.

    Sungur, C.T., Breitenbücher, U., Leymann, F., Wieland, M.: Context-sensitive adaptive production processes. Procedia CIRP 41, 147–152 (2016)

    Google Scholar 

  143. 143.

    Szvetits, M., Zdun, U.: Systematic literature review of the objectives, techniques, kinds, and architectures of models at runtime. Softw. Syst. Model. 15(1), 31–69 (2016)

    Google Scholar 

  144. 144.

    Takahashi, K., Ogata, Y., Nonaka, Y.: A proposal of unified reference model for smart manufacturing. In: 2017 13th IEEE Conference on Automation Science and Engineering (CASE), pp. 964–969. IEEE (2017)

  145. 145.

    The U.S. Advanced Manufacturing Initiative. https://www.nist.gov/sites/default/files/documents/2017/04/28/Molnar_091211.pdf. Accessed 6 June 2018

  146. 146.

    The Industrial Value Chain Initiative. https://iv-i.org/wp/en/about-us/whatsivi/. Accessed 4 June 2018

  147. 147.

    Thoma, A., Kormann, B., Vogel-Heuser, B.: Fault-centric system modeling using SysML for reliability testing. In: 2012 IEEE 17th Conference on Emerging Technologies & Factory Automation (ETFA), pp. 1–8. IEEE (2012)

  148. 148.

    Thomalla, C.S.: Interoperability in manufacturing by semantic integration. In: 2011 International Conference on System Science, Engineering Design and Manufacturing Informatization (ICSEM), vol. 2, pp. 146–149. IEEE (2011)

  149. 149.

    Thramboulidis, K., Christoulakis, F.: UML4IoT—a UML-based approach to exploit IoT in cyber-physical manufacturing systems. Comput. Ind. 82, 259–272 (2016)

    Google Scholar 

  150. 150.

    Torchiano, M., Tomassetti, F., Ricca, F., Tiso, A., Reggio, G.: Relevance, benefits, and problems of software modelling and model driven techniques—a survey in the Italian industry. J. Syst. Softw. 86(8), 2110–2126 (2013)

    Google Scholar 

  151. 151.

    Trappey, A.J.C., Trappey, C.V., Govindarajan, U.H., Sun, J.J., Chuang, A.C.: A review of technology standards and patent portfolios for enabling cyber-physical systems in advanced manufacturing. IEEE Access 4, 7356–7382 (2016)

    Google Scholar 

  152. 152.

    Van Stein, B., Van Leeuwen, M., Wang, H., Purr, S., Kreissl, S., Meinhardt, J., Bäck, T.: Towards data driven process control in manufacturing car body parts. In: 2016 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 459–462. IEEE (2016)

  153. 153.

    Vangheluwe, H., Amaral, V., Giese, H., Broenink, J., Schätz, B., Norta, A., Carreira, P., Lukovic, I., Mayerhofer, T., Wimmer, M., Vallecillo, A.: MPM4CPS: Multi-paradigm modelling for cyber-physical systems. In: Proceedings of the Project Showcase @ STAF 2015, pp. 1–10 (2016)

  154. 154.

    Vogel-Heuser, B., Hess, D.: Guest Editorial Industry 4.0—prerequisites and visions. IEEE Trans. Autom. Sci. Eng. 13(2), 411–413 (2016)

    Google Scholar 

  155. 155.

    Vogel-Heuser, B., Rösch, S., Fischer, J., Simon, T., Ulewicz, S., Folmer, J., et al.: Fault handling in PLC-based Industry 4.0 automated production systems as a basis for restart and self-configuration and its evaluation. J. Softw. Eng. Appl. 9(1), 1 (2016)

    Google Scholar 

  156. 156.

    Walch, M.: Knowledge-driven enrichment of cyber-physical systems for industrial applications using the KbR modelling approach. In: 2017 IEEE International Conference on Agents (ICA), pp. 84–89. IEEE (2017)

  157. 157.

    Wohlin, C., Runeson, P., Höst, M., Ohlsson, M.C., Regnell, B.: Experimentation in Software Engineering. Springer, Berlin (2012)

    Google Scholar 

  158. 158.

    Wollert, J., Lehne, M.: Modeling for ship design and production. In: 1991 Ship Production Symposium Proceedings: Building the Ships and Boats of 2010-The Way Forward, p. 1 (1991)

  159. 159.

    Wortmann, A., Combemale, B., Barais, O.: A systematic mapping study on modeling for Industry 4.0. In: Conference on Model Driven Engineering Languages and Systems (MODELS’17), pp. 281–291. IEEE (2017)

  160. 160.

    Xu, X.: Realization of STEP-NC enabled machining. Robot. Comput. Integr. Manuf. 22(2), 144–153 (2006)

    MathSciNet  Google Scholar 

  161. 161.

    Zadeh, N.S., Lindberg, L., El-Khoury, J., Sivard, G.: Service oriented integration of distributed heterogeneous IT systems in production engineering using information standards and linked data. Model. Simul. Eng. 2017, 9814179 (2017)

  162. 162.

    Zhang, C., Budgen, D.: What do we know about the effectiveness of software design patterns? IEEE Trans. Softw. Eng. 38(5), 1213–1231 (2012)

    Google Scholar 

  163. 163.

    Zhang, Q., Liu, Y., Zhang, Z.: A new method for automatic optimization of drawbead geometry in the sheet metal forming process based on an iterative learning control model. Int. J. Adv. Manuf. Technol. 88, 1845–1861 (2016)

    Google Scholar 

  164. 164.

    Zhao, W.B., Park, Y.H., Lee, H.Y., Jun, C.M., Do Noh, S.: Design and implementation of a PLM system for sustainable manufacturing. In: IFIP International Conference on Product Lifecycle Management, pp. 202–212. Springer, Berlin (2012)

    Google Scholar 

  165. 165.

    Zhiwei, X., Yongxian, L.: Mechanical production line simulation and optimization analysis. In: 2008 IEEE International Conference on Automation and Logistics, pp. 2925–2930 (2008). https://doi.org/10.1109/ICAL.2008.4636677

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Acknowledgements

This work has been partially supported by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development and by the FWF in the Project LEA-xDSML under the Grant Number P 30525-N31.

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Wortmann, A., Barais, O., Combemale, B. et al. Modeling languages in Industry 4.0: an extended systematic mapping study. Softw Syst Model 19, 67–94 (2020). https://doi.org/10.1007/s10270-019-00757-6

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Keywords

  • Industry 4.0
  • Modeling languages
  • Smart manufacturing