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Information Model to Advance Explainable AI-Based Decision Support Systems in Manufacturing System Design

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Managing and Implementing the Digital Transformation (ISIEA 2022)

Abstract

Artificial intelligence is currently being used in more and more areas of production. Be it in the field of industrial robotics, automated quality inspection or cognitive support for employees in production, artificial intelligence contributes to creating smart as well as sustainable manufacturing systems. In the area of manufacturing system design, decision support models are increasingly used to facilitate the work of system designers. In this paper, we address how information models can be used to design explainable artificial intelligence decision support systems. The paper will survey and describe the information that is necessary to communicate manufacturing system design requirements to meet customer needs and use cases. The objective is to propose an information model to express system design requirements with the goal to provide a transparent representation of decisions as well as alternatives of decisions to improve the description of artificial intelligence-based decision support systems during the manufacturing system (re)design phase. The purpose of the information model is to explore the requirements and technical solutions necessary to advance manufacturing systems without losing track of alternatives, and to be able to dynamically adapt them to changing conditions in the market or the production environment.

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References

  1. Lee, J., Davari, H., Singh, J., Pandhare, V.: Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manuf. Lett. 18, 20–23 (2018)

    Google Scholar 

  2. Vickery, A., Rauch, E., Rojas, R.A., Brown, C.A.: Smart data analytics in SME manufacturing–an axiomatic design based conceptual framework. MATEC Web Conf. 301, 1–11 (2019)

    Google Scholar 

  3. Rauch, E.: Industry 4.0+: the next level of intelligent and self-optimizing factories. In: Ivanov, V., Trojanowska, J., Pavlenko, I., Zajac, J., Peraković, D. (eds.) DSMIE 2020. LNME, pp. 176–186. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50794-7_18

    Chapter  Google Scholar 

  4. Hold, P., Erol, S., Reisinger, G., Sihn, W.: Planning and evaluation of digital assistance systems. Procedia Manuf. 9, 143–150 (2017)

    Article  Google Scholar 

  5. Psarommatis, F., Kiritsis, D.: A hybrid decision support system for automating decision making in the event of defects in the era of zero-defect manufacturing. J. Ind. Inf. Integr. 26, 100263 (2022)

    Google Scholar 

  6. Shin, D.: The effects of explainability and causability on perception, trust, and acceptance: implications for explainable AI. Int. J. Hum. Comput. Stud. 146, 102551 (2021)

    Article  Google Scholar 

  7. Henning, K.: Recommendations for implementing the strategic initiative INDUSTRIE 4.0. National Academy of Science and Engineering, Washington, DC, USA (2013)

    Google Scholar 

  8. Tran, K.P.: Artificial intelligence for smart manufacturing: methods and applications. Sensors 21, 5584 (2021). https://doi.org/10.3390/s21165584

    Article  Google Scholar 

  9. Terry, S., Fidan, I., Zhang, Y., Tantawi, K.: Smart manufacturing for energy conservation and savings. In: 2019 NSF ATE Principal Investigators Conference (2019)

    Google Scholar 

  10. Ruiz Garcia, M.A., Rauch, E., Vidoni, R., Matt, D.T.: AI and ML for human-robot cooperation in intelligent and flexible manufacturing. In: Matt, D.T., Modrák, V., Zsifkovits, H. (eds.) Implementing Industry 4.0 in SMEs, pp. 95–127. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-70516-9_3

    Chapter  Google Scholar 

  11. Rauch, E., et al.: AI as an enabler for long-term resilience in manufacturing. White Report. World Manufacturing Forum 2021. https://worldmanufacturing.org/wp-content/uploads/06_Rauch-1.pdf

  12. Krahe, C., Iberl, M., Jacob, A., Lanza, G.: AI-based computer aided engineering for automated product design-a first approach with a multi-view based classification. Procedia CIRP 86, 104–109 (2019)

    Article  Google Scholar 

  13. Milosev, P., Ackovska, N.: AI planning for organizing personal schedules. In: The 8th International Conference for Informatics and Information Technology CIIT (2011)

    Google Scholar 

  14. Arinez, J.F., Chang, Q., Gao, R.X., Xu, C., Zhang, J.: Artificial intelligence in advanced manufacturing: current status and future outlook. J. Manuf. Sci. Eng. 142(11) (2020)

    Google Scholar 

  15. Bao, Y., Ming, Z., Panchal, J.H., Wang, G., Yan, Y.: A reusable and executable information model of experiments on human decision making in systems engineering and design. IEEE Access 8, 27597–27617 (2020)

    Article  Google Scholar 

  16. Neves, P.C., Bernardino, J.R.: The role of big data and business analytics in decision making. In: Human-Computer Interaction and Technology Integration in Modern Society, pp. 226–257. IGI Global (2021)

    Google Scholar 

  17. Tamimi, N., Samani, S., Minaei, M., Harirchi, F.: An artificial intelligence decision support system for unconventional field development design. In: SPE/AAPG/SEG Unconventional Resources Technology Conference. OnePetro (2019)

    Google Scholar 

  18. Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.Z.: XAI—explainable artificial intelligence. Sci. Rob. 4(37), eaay7120 (2019)

    Google Scholar 

  19. Hrnjica, B., Softic, S.: Explainable AI in manufacturing: a predictive maintenance case study. In: Lalic, B., Majstorovic, V., Marjanovic, U., von Cieminski, G., Romero, D. (eds.) APMS 2020. IAICT, vol. 592, pp. 66–73. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57997-5_8

    Chapter  Google Scholar 

  20. Yasmeen, A., Marusich, L.R., Bakdash, J.Z., Zhou, Y., Kantarcioglu, M.: Does explainable artificial intelligence improve human decision-making? In: AAAI (2021)

    Google Scholar 

  21. Tiensuu, H., Tamminen, S., Puukko, E., Röning, J.: Evidence-based and explainable smart decision support for quality improvement in stainless steel manufacturing. Appl. Sci. 11(22), 10897 (2021)

    Article  Google Scholar 

  22. Knapič, S., Malhi, A., Saluja, R., Främling, K.: Explainable artificial intelligence for human decision support system in the medical domain. Mach. Learn. Knowl. Extr. 3, 740–770 (2021). https://doi.org/10.3390/make3030037

    Article  Google Scholar 

  23. Akira: Explainable AI in manufacturing industry (2022). https://www.akira.ai/blog/ai-in-manufacturing-industry/ExplainableAIinmanufacturingimproves,monitoringandsupplychainoptimization. Accessed 23 Apr 2022

  24. ISO: ISO/IEC/IEEE 42010:2011 - systems and software engineering—architecture description. https://www.iso.org/standard/50508.html. Accessed 21 Apr 2022

  25. Opentech AI - Architecture, Ecosystem and Roadmap: Opentech AI - architecture, ecosystem and roadmap. https://opentechai.blog/. Accessed 26 Apr 2022

  26. Yang, L., Cormican, K., Yu, M.: Ontology-based systems engineering: a state-of-the-art review. Comput. Ind. 111, 148–171 (2019)

    Article  Google Scholar 

  27. Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing? Int. J. Hum Comput. Stud. 43(5–6), 907–928 (1995)

    Article  Google Scholar 

  28. Mezhuyev, V.: Ontology based development of domain specific languages for systems engineering. In: 2014 International Conference on Computer and Information Sciences (ICCOINS), pp. 1–6. IEEE (2014)

    Google Scholar 

  29. Sillitto, H.: Sharing systems engineering knowledge through INCOSE: INCOSE as an ultra-large-scale system? Insight 14(1), 20–22 (2011)

    Article  Google Scholar 

  30. Bittner, T., Donnelly, M., Winter, S.: Ontology and semantic interoperability. In: Large-Scale 3D Data Integration, pp. 139–160. CRC Press (2005)

    Google Scholar 

  31. Cruz Segura, Y., Silega Martínez, N., Parra Fernández, A., Gómez Baryolo, O.: Description and analysis of design decisions: an ontological approach. In: Valencia-García, R., Alcaraz-Mármol, G., Del Cioppo-Morstadt, J., Vera-Lucio, N., Bucaram-Leverone, M. (eds.) CITI 2018. CCIS, vol. 883, pp. 174–185. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00940-3_13

    Chapter  Google Scholar 

  32. Kennedy, M.N.: Product Development for the Lean Enterprise: Why Toyota’s System Is Four Times More Productive and How You Can Implement It. CreateSpace Independent Publishing Platform (2003)

    Google Scholar 

  33. Mendonza, P., Fitch, J.A.: Object Based Systems Engineering (2011)

    Google Scholar 

  34. Matt, D.T., Rauch, E.: Continuous improvement of manufacturing systems with the concept of functional periodicity. Key Eng. Mater. 473, 783–790 (2011)

    Google Scholar 

  35. Suh, N.P.: Complexity: Theory and Applications. Oxford University Press, New York (2005)

    Google Scholar 

  36. Boothroyd, G., Dewhurst, P., Knight, W.A.: Product Design for Manufacture and Assembly, 3rd edn. CRC Press, Boca Raton (2010)

    Book  Google Scholar 

  37. Foley, J.T., Cochran, D.S.: Manufacturing system design decomposition: an ontology for data analytics and system design evaluation. Procedia CIRP 60, 175–180 (2017)

    Article  Google Scholar 

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Correspondence to David S. Cochran .

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Cochran, D.S., Smith, J., Mark, B.G., Rauch, E. (2022). Information Model to Advance Explainable AI-Based Decision Support Systems in Manufacturing System Design. In: Matt, D.T., Vidoni, R., Rauch, E., Dallasega, P. (eds) Managing and Implementing the Digital Transformation. ISIEA 2022. Lecture Notes in Networks and Systems, vol 525. Springer, Cham. https://doi.org/10.1007/978-3-031-14317-5_5

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