Hostname: page-component-745bb68f8f-5r2nc Total loading time: 0 Render date: 2025-01-28T04:50:45.999Z Has data issue: false hasContentIssue false
  • English
  • Français

AUTOMATED PRODUCT FUNCTIONALITY AND DESIGN OPTIMIZATION INSTANCING A PRODUCT-SERVICE SYSTEM

Part of: Design Innovation, Information and Knowledge

Published online by Cambridge University Press:  11 June 2020

P. Wolniak ,
B. Sauthoff ,
D. Kloock-Schreiber  and
R. Lachmayer
P. Wolniak*
Affiliation:
Leibniz Universität Hannover, Germany
B. Sauthoff
Affiliation:
Leibniz Universität Hannover, Germany
D. Kloock-Schreiber
Affiliation:
Leibniz Universität Hannover, Germany
R. Lachmayer
Affiliation:
Leibniz Universität Hannover, Germany
*
*wolniak@ipeg.uni-hannover.de

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.

When using product-service systems as a business model, new product development challenges and opportunities arise. Due to the possibility of customizing the product fleet depending on the user-scenarios, more product variants are possible and often necessary. Therefore, this paper presents an approach for the automated functionality and design optimization for user scenario specific use cases. The approach combines an optimization framework with a functional simulation model and a generative design approach CAD model. This results in a robust and simultaneously flexible design environment.

Keywords

design optimisationgenerative design approachknowledge-based engineering (KBE)product-service systems (PSS)
Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 1405 - 1414
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), 2020. Published by Cambridge University Press

References

Bryant Arnold, C.R., Stone, R.B. and McAdams, D.A. (2008), “Memic: An interactive morphological matrix tool for automated concept generation”, IIE Annual Conference and Expo, pp. 1196–1201.Google Scholar
Copeland, J. (2000), “The turing test”, Minds and Machines, Vol. 10 No. 4, pp. 519–539. doi.org/10.1023/A:1011285919106CrossRefGoogle Scholar
Du, K.-L. and Swamy, M.N.S. (2019), Neural networks and statistical learning, Second edition, Springer, London, United Kingdom. https://doi.org/10.1007/978-1-4471-7452-3CrossRefGoogle Scholar
Gembarski, P.C., Bibani, M. and Lachmayer, R. (2016), “Design catalogues: Knowledge repositories for knowledge-based-engineering applications”, Proceedings of International Design Conference, DESIGN, Vol. DS 84, pp. 2007–2016.Google Scholar
Hubka, V. (1976), Theorie der Konstruktionsprozesse, Springer Berlin Heidelberg, Berlin, Heidelberg. doi.org/10.1007/978-3-642-81035-0CrossRefGoogle Scholar
Hvam, L., Mortensen, N.H. and Riis, J. (2008), Product customization, Springer, Berlin, London.Google Scholar
Koller, R. (1994), Konstruktionslehre für den Maschinenbau, Springer Berlin Heidelberg, Berlin, Heidelberg. doi.org/10.1007/978-3-662-08165-5CrossRefGoogle Scholar
La Rocca, G. and van Tooren, M.J.L. (2010), “Knowledge-based engineering to support aircraft multidisciplinary design and optimization”, Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, Vol. 224 No. 9, pp. 1041–1055. doi.org/10.1243/09544100JAERO592CrossRefGoogle Scholar
Li, H. and Lachmayer, R. (2018), “Generative Design Approach for Modeling Creative Designs”, IOP Conference Series: Materials Science and Engineering, Vol. 408, pp. 12035. doi.org/10.1088/1757-899X/408/1/012035CrossRefGoogle Scholar
Meier, H., Roy, R. and Seliger, G. (2010), “Industrial Product-Service systems-IPS2”, CIRP Annals - Manufacturing Technology, Vol. 59 No. 2, pp. 607–627. doi.org/10.1016/j.cirp.2010.05.004CrossRefGoogle Scholar
Mescheder, B. and Sallach, C. (2012), Wettbewerbsvorteile durch Wissen: Knowledge Management, CRM und Change Management verbinden, Springer, Berlin Heidelberg. doi.org/10.1007/978-3-642-27896-9CrossRefGoogle Scholar
Milton, N.R. (2007), Knowledge acquisition in practice, Decision engineering, Springer, London.Google Scholar
Pahl, G. et al. (2007), Engineering design: A systematic approach, 3. ed, Springer, London. doi.org/10.1007/978-1-84628-319-2CrossRefGoogle Scholar
Sabin, D. and Weigel, R. (1998), “Product configuration frameworks-a survey”, IEEE Intelligent Systems, Vol. 13 No. 4, pp. 42–49. doi.org/10.1109/5254.708432CrossRefGoogle Scholar
Sauthoff, B., Gembarski, P.C. and Lachmayer, R. (2016), “Maturity-model-based design of structural components”, Proceedings of International Design Conference, DESIGN, Vol. DS 84, pp. 503–512.Google Scholar
Schreiber, D., Gembarski, P.C. and Lachmayer, R. (Eds.) (2018), Developing a Constraint-Based Solution Space for Product Service Systems.Google Scholar
Schreiber, G., Wielinga, B. and Breuker, J. (1993), KADS: A principled approach to knowledge-based system development / edited by Guus Schreiber, Bob Wielinga, Joost Breuker, Academic, London.Google Scholar
Thomas, O., Walter, P. and Loos, P. (2008), “Product-Service Systems: Konstruktion und Anwendung einer Entwicklungsmethodik”, Wirtschaftsinformatik, Vol. 50 No. 3, pp. 208–219. doi.org/10.1365/s11576-008-0048-7CrossRefGoogle Scholar
Tukker, A. (2004), “Eight types of product-service system: Eight ways to sustainability? Experiences from suspronet”, Business Strategy and the Environment, Vol. 13 No. 4, pp. 246–260. doi.org/10.1002/bse.414CrossRefGoogle Scholar
Wolniak, P., Sauthoff, B. and Lachmayer, R. (2018), “Scaling of Structural Components by Knowledge-Based-Engineering Methods”, The Design Society, Glasgow, UK, May 21-24, 2018, pp. 1757–1768. doi.org/10.21278/idc.2018.0234Google Scholar
Wolniak, P. et al. (2019), “Scaling of Technical Systems Using an Object-Based Modelling Approach”, Proceedings of the Design Society: International Conference on Engineering Design, Vol. 1 No. 1, pp. 1603–1612. http://doi.org/10.1017/dsi.2019.166Google Scholar
Zeigler, B.P., Praehofer, H. and Kim, T.G. (2000), Theory of modeling and simulation: Integrating discrete event and continuous complex dynamic systems, 2nd ed, Academic, San Diego, Calif., London.Google Scholar