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Robustness Evaluation of Product Concepts based on Function Structures

Part of: Robust Design

Published online by Cambridge University Press:  26 July 2019

Stefan Goetz ,
Jonas Hartung ,
Benjamin Schleich  and
Sandro Wartzack

Abstract

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Due to the varying environment conditions as well as the manufacturing induced deviations, the properties of products vary. In order to still meet the increasingly tightening of functional requirements, tolerancing as well as Robust Design practices became integral parts of the product development. However, despite the fact that the robustness of a product is mainly determined by its conceptual design in early design stages, these activities are usually carried out at the end of the design process. In order to overcome this shortcoming, this contribution shows a method that supports the selection of robust principal solutions and thus contributes to the design of product concepts, which are less sensitive to variations. The novelty lies in the adaption and combination of robust design criteria for the quantitative robustness evaluation in the conceptual design stage. First the product characteristics, which are relevant for the product robustness are determined on the basis of the function structure. By using an adopted VMEA and a newly developed evaluation matrix, this allows a thorough robustness evaluation of product concepts. The method is exemplary shown for a lifting table.

Keywords

Robust designConceptual designTolerance representation and managementQFDVMEA
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 3521 - 3530
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

Akao, Y. (2004), Quality function deployment: Integrating customer requirements into product design, Productivity Press, New York.Google Scholar
Almannai, B., Greenough, R. and Kay, J. (2008), “A decision support tool based on QFD and FMEA for the selection of manufacturing automation technologies”, Robotics and Computer-Integrated Manufacturing, Vol. 24 No. 4, pp. 501–507. http://doi.org/10.1016/j.rcim.2007.07.002.Google Scholar
Andersson, P. (1997), “On Robust Design in the Conceptual Design Phase. A Qualitative Approach”, Journal of Engineering Design, Vol. 8 No. 1, pp. 75–89.Google Scholar
Ayağ, Z. (2016), “An integrated approach to concept evaluation in a new product development”, Journal of Intelligent Manufacturing, Vol. 27 No. 5, pp. 991–1005. http://doi.org/10.1007/s10845-014-0930-7.Google Scholar
Cheng Lim, P., Tang, N.K.H. and Jackson, P.M. (1999), “An innovative framework for health care performance measurement”, Managing Service Quality: An International Journal, Vol. 9 No. 6, pp. 423–433. http://doi.org/10.1108/09604529910304125.Google Scholar
Dehnad, K. (1989), Quality Control, Robust Design, and the Taguchi Method, Springer US, Boston. http://doi.org/10.1007/978-1-4684-1472-1.Google Scholar
Ebro, M. and Howard, T.J. (2016), “Robust design principles for reducing variation in functional performance”, Journal of Engineering Design, Vol. 27 No. 1-3, pp. 75–92. http://doi.org/10.1080/09544828.2015.1103844.Google Scholar
Ebro, M., Howard, T.J. and Rasmussen, J.J. (2012), “The Foundation For Robust Design: Enabling Robustness Through Kinematic Design And Design Clarity”, 12th International Design Conference, Dubrovnik, Marjanovic D., Storga M., Pavkovic N., Bojcetic N., pp. 817–826.Google Scholar
Engelhardt, R., Birkhofer, H., Kloberdanz, H. and Mathias, J. (2009), “Uncertainty-Mode- and Effects-Analysis – an Approach to Analyze and Estimate Uncertainty in the Product Life Cycle”, 17th ICED, Palo Alto, 24.-27.08.2009, Norell Bergendahl, M., Grimheden, M., Leifer, L., Skogstad, P., Lindemann, U., pp. 191–202.Google Scholar
Goetz, S., Schleich, B. and Wartzack, S. (2018), “A new approach to first tolerance evaluations in the conceptual design stage based on tolerance graphs”, Procedia CIRP, Vol. 75, pp. 167–172. http://doi.org/10.1016/j.procir.2018.04.030.Google Scholar
Göhler, S.M. (2017), Metric-driven Robust Design – Robustness Quantification of Complex Engineering Systems, PhD Thesis, DTU.Google Scholar
Göhler, S.M. and Howard, T.J. (2015), “The Contradiction Index (CI). A New Metric Combining System Complexity and Robustness for Early Design Stages”, 27th International Conference on Design Theory and Methodology, Boston, 02.-05.08.2015, ASME. http://doi.org/10.1115/DETC2015-47255.Google Scholar
Gremyr, I. and Hasenkamp, T. (2011), “Practices of robust design methodology in practice”, The TQM Journal, Vol. 23 No. 1, pp. 47–58. http://doi.org/10.1108/17542731111097489.Google Scholar
Haskins, C. (2006), Systems Engineering Handbook, INCOSE.Google Scholar
Heling, B., Schleich, B. and Wartzack, S. (2018), “Robust-Design-Optimization of mechanisms based on kinematic requirements considering uncertainties”, Procedia CIRP, Vol. 75, pp. 27–32. http://doi.org/10.1016/j.procir.2018.04.048.Google Scholar
Hermans, J. and Liu, Y. (2013), “Quality Management in the New Product Development. A PPAP Approach”, Quality Innovation Prosperity, Vol. 17 No. 2, http://doi.org/10.12776/QIP.V17I2.150.Google Scholar
Hoffman, H.F. (2014), The Engineering Capstone Course, Springer. http://doi.org/10.1007/978-3-319-05897-9.Google Scholar
Johansson, P., Chakhunashvili, A., Barone, S. and Bergman, B. (2006), “Variation Mode and Effect Analysis. A Practical Tool for Quality Improvement”, Quality and Reliability Engineering International, Vol. 22 No. 8, pp. 865–876. http://doi.org/10.1002/qre.773.Google Scholar
Justel, D., Vidal, R., Arriaga, E., Franco, V. and Val-Jauregi, E. (2007), “Evaluation Method For Selecting Innovative Product Concepts With Greater Potential Marketing Success”, 16th ICED, Paris, 28.-31.07.2007, Bocquet, J.-C.Google Scholar
Kim-Soon, N. (2012), Quality Management and Practices, InTech. http://doi.org/10.5772/36671.Google Scholar
Krehmer, H., Eckstein, R., Lauer, W., Roelofsen, J., Stöber, C., Troll, A., Zanpf, J., Weber, N., Meerkamm, H., Henrich, A., Lindemann, U., Rieg, F. and Wartzack, S. (2010), “Das Forflow-Prozessmodell zur Unterstützung der multidisziplinären Produktentwicklung”, Konstruktion, Vol. 10, pp. 59–68.Google Scholar
Mathias, J., Kloberdanz, H., Eifler, T., Engelhardt, R., Wiebel, M., Birkhofer, H. and Bohn, A. (2011), “Selection Of Physical Effects Based On Disturbances And Robustness Ratios In The Early Phases Of Robust Design”, 18th ICED, Copenhagen, 15.-19.08.2011, Culley, S.J., Hicks, B.J., McAloone, T.C., Howard, T.J. and Malmqvist, J., pp. 324–335.Google Scholar
Matthiassen, B. (1997), Design for robustness and reliability, PhD thesis, DTU.Google Scholar
Mc Dermott, R.E., Mikulak, R.J. and Beauregard, M.R. (2009), The basics of FMEA, Productivity Press, New York.Google Scholar
P. Sullivan, L. (1986), “Quality Function Deployment”, Quality Progress, Vol. 19 No. 6, pp. 39–50.Google Scholar
Pahl, G., Beitz, W., Blessing, L., Feldhusen, J., Grote, K.-H. and Wallace, K. (2007), Engineering Design: A Systematic Approach, Springer, London.Google Scholar
Pfeifer, T. (2002), Quality management: Strategies, methods, techniques, Hanser, München.Google Scholar
Schleich, B., Anwer, N., Zhu, Z., Qiao, L., Mathieu, L. and Wartzack, S. (2014), “A Comparative Study on Tolerance Analysis Approaches”, ISoRD14, Kopenhagen, 14.-15.08.2014, pp. 29–39. http://doi.org/10.4122/dtu:2084.Google Scholar
Song, Y. and Su, Q. (2015), “The relationship between quality management and new product development. Evidence from China”, Operations Management Research, Vol. 8 No. 1-2, pp. 1–14. http://doi.org/10.1007/s12063-014-0096-7.Google Scholar
Suh, N.P. (1998), “Axiomatic Design Theory for Systems”, Research in Engineering Design, Vol. 10 No. 4, pp. 189–209.Google Scholar
Söderberg, R. and Lindkvist, L. (1999), “Computer Aided Assembly Robustness Evaluation”, Journal of Engineering Design, Vol. 10 No. 2, pp. 165–181.Google Scholar
Taguchi, G., Elsayed, E.A. and Hsiang, T.C. (1990), Quality engineering in production systems, McGraw-Hill series in industrial engineering and management science, McGraw-Hill, New York.Google Scholar
Taguchi, G., Yano, H., Chowdhury, S. and Taguchi, S. (Eds.) (2005), Taguchi's quality engineering handbook, John Wiley & Sons, Hoboken, N.J, Livonia, Mich.Google Scholar
Thornton, A.C. (2004), Variation risk management: Focusing quality improvements in product development and production, Wiley, Hoboken, NJ.Google Scholar
Zare Mehrjerdi, Y. (2010), “Quality function deployment and its extensions”, International Journal of Quality & Reliability Management, Vol. 27 No. 6, pp. 616–640. http://doi.org/10.1108/02656711011054524.Google Scholar
Zhai, L.-Y., Khoo, L.-P. and Zhong, Z.-W. (2009), “Design concept evaluation in product development using rough sets and grey relation analysis”, Expert Systems with Applications, Vol. 36 No. 3, pp. 7072–7079. http://doi.org/10.1016/j.eswa.2008.08.068.Google Scholar