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Estimating the Impact of Design Automation: The Influence of Knowledge on Potential Estimation

Part of: ICT for Design

Published online by Cambridge University Press:  26 July 2019

Eugen Rigger ,
Alexander Lutz ,
Kristina Shea  and
Tino Stankovic
Eugen Rigger*
Affiliation:
V-Research GmbH; ETH Zurich, Engineering Design and Computing Laboratory
Alexander Lutz
Affiliation:
ETH Zurich, Engineering Design and Computing Laboratory
Kristina Shea
Affiliation:
ETH Zurich, Engineering Design and Computing Laboratory
Tino Stankovic
Affiliation:
ETH Zurich, Engineering Design and Computing Laboratory
*
Contact: Rigger, Eugen, V-Research GmbH, Design Automation, Austria, [email protected]

Abstract

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Assessing the impact of design automation on design practice prior to its implementation is difficult and subject to uncertainties. One reason for this is the designers' lack of knowledge about design automation. In this work, an industrial case study focusing on conceptual design of hydraulic circuits is conducted to assess the impact of the designers' knowledge on design automation potential estimation. In particular, the impact of demonstrating a prototypical implementation of a design automation application is investigated as a means to enhance the designers' knowledge about design automation. In this respect, a given set of metrics is rated twice to enable a comparative study: prior to and after introducing the design automation prototype. The yielded results show that the knowledge impacts the rating and supports reliability of potential estimation. Further, it is shown that designers acknowledge design automation potential for the early stages of design given sufficient knowledge about design automation. Yet, the results also indicate that careful attention needs to be put on the aspects covered by the prototype in order to avoid biasing participants.

Keywords

Conceptual designComputational design methodsCase studyDesign performanceDesign automation
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 1943 - 1952
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) 2019

References

Amen, R., Rask, I. and Sunnersjö, S. (1999), “Matching Design Tasks to Knowledge-Based Software Tools - When Intuition Does Not Suffice”, Proceeding of DETC99, presented at the 1999 ASME Design Engineering Technical Conferences, Las Vegas, Nevada, pp. 1165–1174.Google Scholar
Arora, J. (2004), “Introduction to Optimum Design”, Academic Press, https://doi.org/10.1016/B978-0-12-064155-0.X5000-9Google Scholar
Blessing, L.T.M. and Chakrabarti, A. (2009), DRM, a Design Research Methodology, Springer, Dordrecht; London, https://doi.org/10.1007/978-1-84882-587-1Google Scholar
Cederfeldt, M., Elgh, F. and others. (2005), “Design automation in SMEs–current state, potential, need and requirements”, Proceedings of ICED 05, presented at the 15th International Conference on Engineering Design, Melbourne, Australia, 15.-18.08. 2005Google Scholar
Emberey, C.L., Milton, N., Berends, J.P.T.J., van Tooren, M.J.. and van der Elst, S.W. (2007), “Application of Knowledge Engineering Methodologies to Support Engineering Design Application Development in Aerospace”, 7th AIAA Aviation Technology, Integration and Operations Conference (ATIO), Belfast, Northern Ireland. https://doi.org/10.2514/6.2007-7708Google Scholar
Frank, G., Entner, D., Prante, T., Khachatouri, V. and Schwarz, M. (2014), “Towards a Generic Framework of Engineering Design Automation for Creating Complex CAD Models”, International Journal On Advances in Systems and Measurements, Vol. 7 No. 1 and 2, pp. 179–192.Google Scholar
Likert, R. (1932), “A technique for the measurement of attitudes”, Archives of Psychology, Vol. 22 No. 140, pp. 55–55.Google Scholar
Mulder, B., La Rocca, G., Schut, J. and Verhagen, Wim J C. (2015), “A methodological approach for the optimisation of the product development process by the application of design automation”, CEAS 2015, presented at the 5th CEAS Air and Space Conference Challenges in European Aerospace, CEAS, Delft.Google Scholar
Münzer, C. and Shea, K. (2017), “Simulation-Based Computational Design Synthesis Using Automated Generation of Simulation Models From Concept Model Graphs”, Journal of Mechanical Design, Vol. 139 No. 7, p. 071101. https://doi.org/10.1115/1.4036567Google Scholar
Pal, P. and Ghosh, K.K. (2017), “Estimating digitization efforts of complex product realization processes”, The International Journal of Advanced Manufacturing Technology, https://doi.org/10.1007/s00170-017-1442-3Google Scholar
Parks, M., Bansal, S. and Zilberman, D. (2016), “Fit-risk in development projects: role of demonstration in technology adoption”, Environment and Development Economics, Vol. 21 No. 06, pp. 742–766. https://doi.org/10.1017/s1355770x16000188Google Scholar
Rigger, E., Stanković, T. and Shea, K. (2018), “Task Categorization for Identification of Design Automation Opportunities”, Journal of Engineering Design, https://doi.org/10.1080/09544828.2018.1448927Google Scholar
Rigger, E. and Vosgien, T. (2018), “Design Automation State of Practice - Potential and Opportunities”, Proceedings of the 15th International DESIGN Conference, presented at the DESIGN 2018, https://doi.org/10.21278/idc.2018.0537Google Scholar
Schut, E.J., Kosman, S. and Curran, R. (2013), “A Value Scan Methodology to Improve Industrial Operations”, in Stjepandić, J., Rock, G. and Bil, C. (Eds.), Concurrent Engineering Approaches for Sustainable Product Development in a Multi-Disciplinary Environment, Springer London, London, pp. 411–423. https://doi.org/10.1007/978-1-4471-4426-7_36Google Scholar
van der Velden, C., Bil, C. and Xu, X. (2012), “Adaptable methodology for automation application development”, Advanced Engineering Informatics, Vol. 26 No. 2, pp. 231–250. https://doi.org/10.1016/j.aei.2012.02.007Google 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. 307–321. https://doi.org/10.1016/j.aei.2015.03.003Google Scholar
Wynn, D.C. and Clarkson, P.J. (2018), “Process models in design and development”, Research in Engineering Design, https://doi.org/10.1007/s00163-017-0262-7Google Scholar