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Design Automation Systems for the Product Development Process: Reflections from Five Industrial Case Studies

Published online by Cambridge University Press:  26 May 2022

O. Vidner*
Affiliation:
Linköping University, Sweden
C. Wehlin
Affiliation:
Linköping University, Sweden
A. Wiberg
Affiliation:
Linköping University, Sweden

Abstract

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This paper presents five industrial cases where design automation (DA) systems supported by design optimization has been developed, and aims to summarize the lesson learned and identify needs for future development of such projects. By mapping the challenges during development and deployment of the systems, common issues were found in technical areas such as model integration and organizational areas such as knowledge transfer. The latter can be seen as a two-layered design paradox; one for the product that the DA system is developed for, and one for the development of the DA system.

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

Blecker, T., Abdelkafi, N., 2006. Mass customization: state-of-the-art and challenges, in: Blecker, T., Friedrich, G. (Eds.), Mass Customization: Challenges and Solutions, International Series in Operations Research & Management Science. Kluwer Academic Publishers, Boston, pp. 125. 10/c73rrmGoogle Scholar
Cederfeldt, M., Elgh, F., 2005. Design automation in SMEs – Current state, potential, need and requirements. Proceedings ICED 05, the 15th International Conference on Engineering Design DS 35, 115.Google Scholar
Forza, C., Salvador, F., 2008. Application support to product variety management. International Journal of Production Research 46, 817836. 10/d3nvpvCrossRefGoogle Scholar
Hvam, L., Mortensen, N.H., Riis, J., 2008. Product customization. Springer, Berlin. 10/cj7n5pGoogle Scholar
ISO/IEC/IEEE, 2022. ISO/IEC/IEEE International Standard - Software engineering - Software life cycle processes - Maintenance (International Standard No. 14764:2022(E)). 10/hg23Google Scholar
Kristjansdottir, K., Shafiee, S., Hvam, L., Bonev, M., Myrodia, A., 2018. Return on investment from the use of product configuration systems – a case study. Computers in Industry 100, 5769. 10/gdz2ttCrossRefGoogle Scholar
Kuhn, O., Dusch, T., Ghodous, P., Collet, P., 2012. Framework for the support of knowledge-based engineering template update. Computers in Industry 63, 423432. 10/f325vrCrossRefGoogle Scholar
La Rocca, G., 2012. Knowledge based engineering: between AI and CAD. Review of a language based technology to support engineering design. Advanced Engineering Informatics 26, 159179. 10/f39n5wGoogle Scholar
Lyly, J., Hartwig, S., Nord, G., 2018. Epiroc Mobile Miner: hard rock cutting is now a reality. Presented at the Bergdagarna 2018, Svenska Bergteknikföreningen, pp. 277290.Google Scholar
Martins, J.R.R.A., Ning, A., 2021. Engineering design optimization. Cambridge University Press. 10/hfkpGoogle Scholar
Consortium, MOKA, 2001. Managing engineering knowledge: MOKA: methodology for knowledge based engineering applications. Professional Engineering Publishing Limited, London, Bury St. Edmunds.Google Scholar
Papalambros, P.Y., Wilde, D.J., 2000. Principles of optimal design: modeling and computation, 2nd ed. Cambridge university press.CrossRefGoogle Scholar
Piller, F., 2004. Mass Customization: Reflections on the State of the Concept. International Journal of Flexible Manufacturing Systems 16, 313334. 10/cphjxwGoogle Scholar
Poorkiany, M., 2015. Support maintenance of design automation systems: a framework to capture, structure and access design rationale (Licentiate Thesis). School of Engineering, Jönköping.Google Scholar
Poot, L.P., Wehlin, C., Tarkian, M., Ölvander, J., 2020. Integrating sales and design: applying CAD configurators in the product development process, in: Proceedings of the Design Society: DESIGN Conference. pp. 345354. 10/gjsffrGoogle Scholar
Rasmussen, J.B., Myrodia, A., Hvam, L., Mortensen, N.H., 2018. Cost of not maintaining a product configuration system. IJIEM 9, 205214. 10/gnnq55Google Scholar
Rigger, E., Shea, K., Stankovic, T., 2018. Task categorisation for identification of design automation opportunities. Journal of Engineering Design 29, 131159. 10/gmf64qCrossRefGoogle Scholar
Rigger, E., Vosgien, T., 2018. Design automation state of practice - potential and opportunities. Presented at the 15th International Design Conference, pp. 441452. 10/gngrvmGoogle Scholar
Salvador, F., de Holan, P.M., Piller, F., 2009. Cracking the Code of Mass Customization. MIT Sloan Management Review 50, 7178.Google Scholar
Sobieszczanski-Sobieski, J., Morris, A., van Tooren, M.J.L., 2015. Multidisciplinary design optimization supported by knowledge based engineering. John Wiley & Sons, Ltd., Chichester, UK.CrossRefGoogle Scholar
Tseng, M.M., Jiao, J., Merchant, M.E., 1996. Design for Mass Customization. CIRP Annals - Manufacturing Technology 45, 153156. 10/fp7np4CrossRefGoogle Scholar
Ullman, D.G., 2010. The mechanical design process, 4th ed, McGraw-Hill series in mechanical engineering. McGraw-Hill Higher Education, Boston.Google Scholar
Ulrich, K.T., Eppinger, S.D., 2016. Product design and development, 6th ed. McGraw-Hill Education, New York, NY.Google Scholar
Vidner, O., Pettersson, R., Persson, J.A., Ölvander, J., 2021a. Multidisciplinary design optimization of a Mobile Miner using the OpenMDAO platform. Proc. Des. Soc. 1, 22072216. 10/grrpCrossRefGoogle Scholar
Vidner, O., Wehlin, C., Persson, J.A., Ölvander, J., 2021b. Configuring customized products with design optimization and value-driven design. Proc. Des. Soc. 1, 741750. 10/grrmCrossRefGoogle Scholar
Wehlin, C., 2021. Optimization-based configurators in the product development process (Licentiate Thesis). Linköping University, Linköping. 10/gkmw79Google Scholar
Wehlin, C., Persson, J.A., Ölvander, J., 2020. Multi-objective optimization of hose assembly routing for vehicles, in: Proceedings of the Design Society: DESIGN Conference. Cambridge University Press, pp. 471480. 10/ghfggcGoogle Scholar
Wehlin, C., Vidner, O., Poot, L.P., Tarkian, M., 2021. Integrating sales, design and production: a configuration system for automation in mass customization, in: Volume 3B: 47th Design Automation Conference (DAC). Presented at the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers. 10/gnrxwdGoogle Scholar
Wiberg, A., Ericsson, L., Persson, J.A., Ölvander, J., 2022. Additive Manufacturing in fluid power with a novel application to hydraulic pump design. Submitted to Rapid Prototyping Journal.Google Scholar
Wiberg, A., Persson, J., Ölvander, J., 2021. An optimisation framework for designs for additive manufacturing combining design, manufacturing and post-processing. RPJ 27, 90105. 10/gngrvnCrossRefGoogle Scholar
Wiberg, A., Persson, J., Ölvander, J., 2019. Design for additive manufacturing – a review of available design methods and software. RPJ 25, 10801094. 10/gjbhrpCrossRefGoogle Scholar
Wortmann, J.C., 1983. A classification scheme for master production scheduling, in: Wilson, B., Berg, C.C., French, D. (Eds.), Efficiency of Manufacturing Systems. Springer US, Boston, MA, pp. 101109. 10/hg43CrossRefGoogle Scholar
Yin, R.K., 2003. Case study research: design and methods, 3rd ed, Applied social research methods. Sage Publications, Thousand Oaks, Calif.Google Scholar