Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-23T06:24:30.648Z Has data issue: false hasContentIssue false

A Survey on the Challenges Hindering the Application of Data Science, Digital Twins and Design Automation in Engineering Practice

Published online by Cambridge University Press:  26 May 2022

S. Rädler*
Affiliation:
V-Research GmbH, Austria TU Wien, Austria
E. Rigger
Affiliation:
V-Research GmbH, Austria

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Digital Engineering is an emerging trend and aims to support engineering design by integrating computational technologies like design automation, data science, digital twins, and product lifecycle management. To enable alignment of industrial practice with state of the art, an industrial survey is conducted to capture the status and identify obstacles that hinder implementation in the industry. The results show companies struggle with missing know-how and available experts. Future work should elaborate on methods that facilitate the integration of Digital Engineering in design practice.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

References

Berman, F., Rutenbar, R., Hailpern, B., Christensen, H., Davidson, S., Estrin, D., Franklin, M., et al. . (2018), “Realizing the potential of data science”, Communications of the ACM, Vol. 61 No. 4, pp. 6772.CrossRefGoogle Scholar
Bitrus, S., Velkavrh, I. and Rigger, Eugen. (2020), “Applying an Adapted Data Mining Methodology (DMME) to a Tribological Optimisation Problem”, International Data Science Conference (IDSC 2020), presented at the international Data Science Conference (iDSC 2020).Google Scholar
Blessing, L.T.M. and Chakrabarti, A. (2009), DRM, a Design Research Methodology, Springer, Dordrecht; London, available at: 10.1007/978-1-84882-587-1.CrossRefGoogle Scholar
Camburn, B., He, Y., Raviselvam, S., Luo, J. and Wood, K. (2020), “Machine Learning-Based Design Concept Evaluation”, Journal of Mechanical Design, Vol. 142 No. 3, available at:10.1115/1.4045126.CrossRefGoogle Scholar
Cao, L. (2017), “Data Science: A Comprehensive Overview”, ACM Computing Surveys, Vol. 50 No. 3, pp. 142.CrossRefGoogle Scholar
Creswell, J.W. (2009), Research Design - Qualitative, Quantitative, and Mixed Methods Approaches, 3rd edition., SAGE Publications Ltd., available at: https://uk.sagepub.com/en-gb/eur/research-design/book237357 (accessed 26 April 2018).Google Scholar
Curran, R., Verhagen, W.J.C., van Tooren, M.J.L. and van der Laan, Ton.H. (2010), “A multidisciplinary implementation methodology for knowledge based engineering: KNOMAD”, Expert Systems with Applications, Vol. 37 No. 11, pp. 73367350.Google Scholar
Dogan, A. and Birant, D. (2021), “Machine learning and data mining in manufacturing”, Expert Systems with Applications, Vol. 166, p. 114060.CrossRefGoogle Scholar
Duffy, A.H.B. (2005), “Design process and performance”, Engineering Design-Theory and Practice, Cambridge, U.K., pp. 7685.Google Scholar
Ehrlenspiel, K., Kiewert, A. and Lindemann, U. (2007), Cost-Efficient Design, edited by Hundal, M.S., Springer-Verlag, Berlin Heidelberg, available at: https://www.springer.com/de/book/9783540346470 (accessed 25 September 2019).CrossRefGoogle Scholar
Grieves, M. and Vickers, J. (2017), “Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems”, in Kahlen, F.-J., Flumerfelt, S. and Alves, A. (Eds.), Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, Springer International Publishing, Cham, pp. 85113.Google Scholar
Hannappel, R. (2017), “The impact of global warming on the automotive industry”, AIP Conference Proceedings, American Institute of Physics, Vol. 1871 No. 1, p. 060001.Google Scholar
Huang, J., Gheorghe, A., Handley, H., Pazos, P., Pinto, A., Kovacic, S., Collins, A., et al. . (2020), “Towards digital engineering: the advent of digital systems engineering”, International Journal of System of Systems Engineering, Inderscience Publishers, Vol. 10 No. 3, pp. 234261.CrossRefGoogle Scholar
Huber, S., Wiemer, H., Schneider, D. and Ihlenfeldt, S. (2019), “DMME: Data mining methodology for engineering applications - a holistic extension to the CRISP-DM model”, Procedia CIRP, Vol. 79, pp. 403408.Google Scholar
Jiang, S., Hu, J., Wood, K.L. and Luo, J. (2022), “Data-Driven Design-By-Analogy: State-of-the-Art and Future Directions”, Journal of Mechanical Design, Vol. 144 No. 2, p. 020801.Google Scholar
Kristjansdottir, K., Shafiee, S., Hvam, L., Forza, C. and Mortensen, N.H. (2018), “The main challenges for manufacturing companies in implementing and utilizing configurators”, Computers in Industry, Vol. 100, pp. 196211.Google Scholar
Lankhorst, M. (2017), Enterprise Architecture at Work, 4th ed., Springer Berlin Heidelberg, Berlin, Heidelberg, available at:10.1007/978-3-642-01310-2.Google Scholar
Lankhorst, M.M., Proper, H.A. and Jonkers, H. (2010), “The Anatomy of the ArchiMate Language”:, International Journal of Information System Modeling and Design, Vol. 1 No. 1, pp. 132.CrossRefGoogle Scholar
Lapalme, J., Gerber, A., Van der Merwe, A., Zachman, J., Vries, M.D. and Hinkelmann, K. (2016), “Exploring the future of enterprise architecture: A Zachman perspective”, Computers in Industry, Vol. 79, pp. 103113.CrossRefGoogle Scholar
Lu, Y., Liu, C., Wang, K.I.-K., Huang, H. and Xu, X. (2020), “Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues”, Robotics and Computer-Integrated Manufacturing, Vol. 61, p. 101837.CrossRefGoogle Scholar
Meyer, T., Gracht, H.A. von der and Hartmann, E. (2022), “How Organizations Prepare for the Future: A Comparative Study of Firm Size and Industry”, IEEE Transactions on Engineering Management, Vol. 69 No. 2, pp. 511523.CrossRefGoogle Scholar
Miller, A.M., Alvarez, R. and Hartman, N. (2018), “Towards an extended model-based definition for the digital twin”, Computer-Aided Design and Applications, Vol. 15 No. 6, pp. 880891.CrossRefGoogle Scholar
Ozili, P.K. and Arun, T. (2020), Spillover of COVID-19: Impact on the Global Economy, SSRN Scholarly Paper No. ID 3562570, Social Science Research Network, Rochester, NY, available at:10.2139/ssrn.3562570.Google Scholar
Patton, M.Q. (2002), “Two Decades of Developments in Qualitative Inquiry: A Personal, Experiential Perspective”, Qualitative Social Work, SAGE Publications, Vol. 1 No. 3, pp. 261283.Google Scholar
Rädler, S. and Rigger, E. (2020), “Participative Method to identify Data-Driven Design Use Cases”, Proceedings of the International Conference on PLM, presented at the Internation conference on product lifecycle management, Springer, Rapperswil, CH, available at:10.1007/978-3-030-62807-9_54.Google Scholar
Riepl, W. (2012), “CRISP-DM: Ein Standard-Prozess-Modell für Data Mining”, Statistik Dresden, available at: https://statistik-dresden.de/archives/1128 (accessed 7 March 2020).Google Scholar
Rigger, E., Lutz, A., Shea, K. and Stanković, T. (2019a), “Estimating the Impact of Design Automation: the Influence of Knowledge on Potential Estimation”, Proceedings of ICED 19, presented at the ICED 2019, Design Society, Delft.Google Scholar
Rigger, E., Stanković, T. and Shea, K. (2018), “Task Categorization for Identification of Design Automation Opportunities”, Journal of Engineering Design, available at:10.1080/09544828.2018.1448927.Google Scholar
Rigger, E. and Vosgien, T. (2018), “Design Automation State of Practice - Potential and Opportunities”, DS 92: Proceedings of the DESIGN 2018 15th International Design Conference, presented at the 15th International Design Conference, pp. 441–452.CrossRefGoogle Scholar
Rigger, E., Vosgien, T., Shea, K. and Stankovic, T. (2019b), “A top-down method for the derivation of metrics for the assessment of design automation potential”, Journal of Engineering Design, pp. 131.CrossRefGoogle Scholar
Russegger, S., Freudenthaler, B., Günter, G., Kieseberg, P., Stern, H. and Strohmeier, F. (2015), “Big Data und Data-driven Business für KMU”, Salzburg Research Forschungsgesellschaft, May, available at: https://www.salzburgresearch.at/publikation/big-data-und-data-driven-business-fuer-kmu/ (accessed 2 July 2021).Google Scholar
Schneider, H.W., Brunner, P., Demirol, D., Luptacik, P. and Landendinger, P. (2020), “IT-Qualifikationen für die österreichische Wirtschaft”, Vol. IWI-Studie, p. 115.Google Scholar
Shen, J., Wei, Y. and Yang, Z. (2017), “The impact of environmental regulations on the location of pollution-intensive industries in China”, Journal of Cleaner Production, Vol. 148, available at:10.1016/j.jclepro.2017.02.050.CrossRefGoogle Scholar
Shi, B., Yang, H., Wang, J. and Zhao, J. (2016), “City Green Economy Evaluation: Empirical Evidence from 15 Sub-Provincial Cities in China”, Sustainability, Multidisciplinary Digital Publishing Institute, Vol. 8 No. 6, p. 551.Google Scholar
Spence, M. (2011), “The Impact of Globalization on Income and Employment: The Downside of Integrating Markets”, Foreign Affairs, Vol. 90, p. 28.Google Scholar
“Stack Overflow Developer Survey 2021”. (2021), Stack Overflow, May, available at: https://insights.stackoverflow.com/survey/2021/ (accessed 27 October 2021).Google Scholar
Stanula, P., Ziegenbein, A. and Metternich, J. (2018), “Machine learning algorithms in production: A guideline for efficient data source selection”, Procedia CIRP, Vol. 78, pp. 261266.Google Scholar
Stark, J. (2011), “Product Lifecycle Management”, Product Lifecycle Management, Springer London, pp. 116.Google Scholar
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H. and Sui, F. (2018), “Digital twin-driven product design, manufacturing and service with big data”, The International Journal of Advanced Manufacturing Technology, Vol. 94 No. 9, pp. 35633576.Google Scholar
The Open Group. (2019), “ArchiMate® 3.1 Specification”, available at: https://pubs.opengroup.org/architecture/archimate3-doc/ (accessed 18 March 2020).Google Scholar
Tsui, R., Davis, D. and Sahlin, J. (2018), “Digital Engineering Models of Complex Systems using Model-Based Systems Engineering (MBSE) from Enterprise Architecture (EA) to Systems of Systems (SoS) Architectures & Systems Development Life Cycle (SDLC)”, INCOSE International Symposium, John Wiley & Sons, Ltd, Vol. 28 No. 1, pp. 760776.Google Scholar
Verhagen, W.J.C., de Vrught, B., Schut, J. and Curran, R. (2015), “A method for identification of automation potential through modelling of engineering processes and quantification of information waste”, Advanced Engineering Informatics, Vol. 29, pp. 307321.Google Scholar
Wiemer, H., Drowatzky, L. and Ihlenfeldt, S. (2019), “Data Mining Methodology for Engineering Applications (DMME) - A Holistic Extension to the CRISP-DM Model”, Applied Sciences, Vol. 9 No. 12, p. 2407.CrossRefGoogle Scholar
Zachman, J.A. (1987), “A framework for information systems architecture”, IBM Systems Journal, Vol. 26 No. 3, pp. 276292.CrossRefGoogle Scholar
Zheng, P., Zhao, G., Torres, V.H. and Rios, J. (2012), “A knowledge-based approach to integrate Fuzzy Conceptual Design Tools and MOKA into a CAD system”, 2012 7th International Conference on Computing and Convergence Technology (ICCCT), presented at the 2012 7th International Conference on Computing and Convergence Technology (ICCCT), pp. 1285–1291.Google Scholar