Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-25T23:17:27.908Z Has data issue: false hasContentIssue false
  • English
  • Français

ROBUST MULTI-OBJECTIVE OPTIMIZATION IN PRODUCT DEVELOPMENT WITH RESPECT TO USER-SCENARIO AND MANUFACTURING UNCERTAINTIES

Published online by Cambridge University Press:  27 July 2021

Philipp Wolniak ,
Jakob Cramer  and
Roland Lachmayer
Philipp Wolniak*
Affiliation:
Leibniz University Hannover
Jakob Cramer
Affiliation:
Leibniz University Hannover
Roland Lachmayer
Affiliation:
Leibniz University Hannover
*
Wolniak, Philipp, Leibniz University Hannover, Institute of Product Development, 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.

In product development, user-scenarios are a way of tailoring requirements to defined customer groups. Furthermore, a product design often involves multiple conflicting objectives that are analyzed within an iterative process. The models typically used for the analysis often do not accurately reflect the real-world representation. This can be alleviated by finding robust product designs. While usually uncertainties due to manufacturing tolerances are investigated, we additionally consider uncertainties in the user-scenario. Therefore, we present a robustness evaluation in a multi-objective numerical optimization in product development. For this, we consider manufacturing tolerances using an adjusted Latin Hypercube Sampling as well as deviations in the user-scenario by means of a Gaussian distribution. In the case study, we present the robust development of a customer specific coffee machine, where we show the robustness evaluation and the impact of the proposed adjustments. The advantage of the presented process is a product design tailored to the customer's requirements under specified uncertainties. In addition, this enables a time benefit in the product development due to the automated analysis used in the optimization.

Keywords

Robust designMulti-objective optimizationUser-scenarioUncertaintyComputational design methods
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 2591 - 2600
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), 2021. Published by Cambridge University Press

References

REFERENCES

Alinejad, F. and Botto, D. (2019), “Innovative adaptive penalty in surrogate-assisted robust optimization of blade attachments”, Acta Mechanica, Vol. 230 No. 8, pp. 2735–2750.CrossRefGoogle Scholar
Branke, J. (1998), “Creating robust solutions by means of evolutionary algorithms”, in Eiben, A.E. (Ed.), Parallel problem solving from nature: 5th international conference, Amsterdam, The Netherlands, September 27 - 30, 1998 ; proceedings, Lecture Notes in Computer Science, Vol. 1498, Springer, Berlin, pp. 119–128.Google Scholar
Brockmöller, T., Li, H., Gembarski, P.C. and Lachmayer, R. (2020), “An Investigation of a Generative Parametric Design Approach for a Robust Solution Development”, Proceedings of the Design Society: DESIGN Conference, pp. 315–324.Google Scholar
Chalupnik, M.J., Wynn, D.C. and Clarkson, P.J. (2009), “Approaches to Mitigate the Impact of Uncertainty in Development Processes”, DS 58-1: Proceedings of ICED 09, the 17th International Conference on Engineering Design, Vol. 1, Design Processes, Palo Alto, CA, USA, 24.-27.08.2009, pp. 459–470.Google Scholar
Chang, P.B., Williams, B.J., Santner, T.J., Notz, W.I. and Bartel, D.L. (1999), “Robust optimization of total joint replacements incorporating environmental variables”, Journal of biomechanical engineering, Vol. 121 No. 3, pp. 304–310.CrossRefGoogle ScholarPubMed
Chen, W., Allen, J.K., Tsui, K.-L. and Mistree, F. (1996), “A Procedure for Robust Design: Minimizing Variations Caused by Noise Factors and Control Factors”, Journal of Mechanical Design, Vol. 118 No. 4, pp. 478–485.CrossRefGoogle Scholar
Deb, K. and Gupta, H. (2005), “Searching for Robust Pareto-Optimal Solutions in Multi-objective Optimization”, in Coello, Coello, Hernández Aguirre, C.A., and Zitzler, A., E. (Eds.), Evolutionary multi-criterion optimization: Third international conference, EMO 2005, Guanajuato, Mexico, March 9 - 11, 2005 ; proceedings, Lecture Notes in Computer Science, Vol. 3410, Springer, Berlin, pp. 150–164.CrossRefGoogle Scholar
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. (2002), “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Transactions on Evolutionary Computation, Vol. 6 No. 2, pp. 182–197.CrossRefGoogle Scholar
Filippidou, D. (1998), “Designing with scenarios: A critical review of current research and practice”, Requirements Engineering, Vol. 3 No. 1, pp. 1–22.CrossRefGoogle Scholar
Gunawan, S. and Azarm, S. (2005), “Multi-objective robust optimization using a sensitivity region concept”, Structural and Multidisciplinary Optimization, Vol. 29 No. 1, pp. 50–60.CrossRefGoogle Scholar
He, Z., Yen, G.G. and Lv, J. (2020), “Evolutionary Multiobjective Optimization With Robustness Enhancement”, IEEE Transactions on Evolutionary Computation, Vol. 24 No. 3, pp. 494–507.CrossRefGoogle Scholar
Heling, B., Schleich, B. and Wartzack, S. (2018), “Robust-Design-Optimization of mechanisms based on kinematic requirements considering uncertainties”, Procedia CIRP - 15th CIRP Conference on Computer Aided Tolerancing – CIRP CAT 2018, Vol. 75, pp. 27–32.CrossRefGoogle Scholar
Jensen, F.V. and Nielsen, T.D. (2007), Bayesian networks and decision graphs, Information Science and Statistics, Softcover reprint of the hardcover 2nd ed. 2007, Springer, Berlin.Google Scholar
Jin, R., Du, X. and Chen, W. (2003), “The use of metamodeling techniques for optimization under uncertainty”, Structural and Multidisciplinary Optimization, Vol. 25 No. 2, pp. 99–116.CrossRefGoogle Scholar
Lachmayer, R., Mozgova, I. and Da Silva de Siqueira, R. (2017), “Development of a Topology Optimization Method for Tailored Forming Multi-material Design”, in Procceedings of the 24th ABCM International Congress of Mechanical Engineering, 12/03/2017, ABCM.CrossRefGoogle Scholar
Lee, K.-H. and Park, G.-J. (2001), “Robust optimization considering tolerances of design variables”, Computers & Structures, Vol. 79 No. 1, pp. 77–86.CrossRefGoogle Scholar
Mckay, M.D., Beckman, R.J. and Conover, W.J. (2000), “A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code”, Technometrics, Vol. 42 No. 1, pp. 55–61.CrossRefGoogle Scholar
Meier, H., Roy, R. and Seliger, G. (2010), “Industrial Product-Service Systems—IPS 2”, CIRP Annals, Vol. 59 No. 2, pp. 607–627.CrossRefGoogle Scholar
Paenke, I., Branke, J. and Jin, Y. (2006), “Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation”, IEEE Transactions on Evolutionary Computation, Vol. 10 No. 4, pp. 405–420.CrossRefGoogle Scholar
Park, G.-J. and Lee, K.-H. (2005), “A Global Robust Optimization Using the Kriging Based Approximation Model”, Transactions of the Korean Society of Mechanical Engineers A, Vol. 29 No. 9, pp. 1243–1252.CrossRefGoogle Scholar
Renzi, C., Leali, F. and Di Angelo, L. (2017), “A review on decision-making methods in engineering design for the automotive industry”, Journal of Engineering Design, Vol. 28 No. 2, pp. 118–143.CrossRefGoogle Scholar
Riesenfeld, R.F., Haimes, R. and Cohen, E. (2015), “Initiating a CAD renaissance: Multidisciplinary analysis driven design”, Computer Methods in Applied Mechanics and Engineering, Vol. 284, pp. 1054–1072.CrossRefGoogle Scholar
Schreiber, D., Gembarski, P.C. and Lachmayer, R. (2018), “Developing a Constraint-Based Solution Space for Product Service Systems”, Proceedings of the 8th International Conference on Mass Customization and Personalization - Community of Europe (MCP-CE 2020).Google Scholar
Venanzi, I. and Materazzi, A.L. (2013), “Robust optimization of a hybrid control system for wind-exposed tall buildings with uncertain mass distribution”, Smart Structures and Systems, Vol. 12 No. 6, pp. 641–659.CrossRefGoogle Scholar
Wolniak, P., Kloock-Schreiber, D., Sauthoff, B. and Lachmayer, R. (2020a), “Integrating Architectural Design Changes in Computer-Aided Design Optimization”, Proceedings of the 9 International Conference on Mass Customization and Personalization - Community of Europe (MCP-CE 2020).Google Scholar
Wolniak, P., Sauthoff, B., Kloock-Schreiber, D. and Lachmayer, R. (2020b), “Automated Product Functionality and Design Optimization Instancing a Product-Service System”, Proceedings of the Design Society: DESIGN Conference, Vol. 1, pp. 1405–1414.CrossRefGoogle Scholar
Wynn, D.C. and Eckert, C.M. (2017), “Perspectives on iteration in design and development”, Research in Engineering Design, Vol. 28 No. 2, pp. 153–184.CrossRefGoogle Scholar