Abstract
In an increasingly challenging environment of growing competition and stricter legal regulations, the concept of lightweight design gets more and more important for the automotive industry. Usually, the goals of lightweight tend to conflict with the robustness of the designs. The scattering of the geometry within the manufacturing tolerance leads to a divergence between the test bench results and the prediction from simulation. For an optimized lightweight design, this deviation will often lead to a violation of the requirements. In this case, the part has to be re-designed to improve its robustness, causing an additional time expense, higher costs and a higher weight.
This paper presents a new way to answer the conflict of objectives between lightweight and robustness. By the integration of manufacturing tolerances in an early stage of the product design, the robustness of a part can be improved without increasing its weight. A parametric CAE model is used to represent all deviations of the geometry and material properties within the tolerance range. Additionally, an analytic expression of the relevant simulation results is built. These tools are not only used to predict exactly the scattering of the test bench result, but also allow optimizing the tolerance ranges in order to obtain a 100% satisfaction of the requirements in every tolerated configuration.
The optimized tolerance ranges make it possible transferring the weight advantage generated in the structural optimization into the design of prototypes. Through a better prediction of the test bench results in the simulation, unnecessary development loops can be avoided. The presented procedure is the first one that integrates manufacturing tolerances within the simulation with the objective of optimizing the tolerance ranges beyond a simple parameter optimization. Thereby, new weight potentials are allowed, and the development process can be substantially shortened.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, Z.: Robust design optimization: ensuring robustness against uncertainty in structural design. All issertations, Paper 1157 (2013)
Jourdan, F.A.: Computerunterstützte Toleranzfestlegung zur Qualitätssicherung und Optimierung der Fertigungskosten. Dissertation, ETH Zürich (2003)
Valliappan, S., Chee, C.K.: Aging degradation of mechanical structures. J. Mech. Mater. Struct. 3, 10 (2008)
Kang, Z.: Robust design optimization of structures under uncertainties. Dissertation, Universität Stuttgart (2005)
Voß, R.: Toleranzanalyse komplexer Gussbauteile mittels stochastischer Simulation der Fertigungseinflüsse. Dissertation, Universität Erlangen-Nürnberg (2011)
Stark, R.: Entwicklung eines mathematischen Toleranzenmodells zur Integration in (3D-) CAD-Systeme. Schriftenreihe Produktionstechnik, Universität des Saarlandes (1994)
Jörgensen-Rechter, S.: Rechnergestützte Analyse von Mass-, Form- und Lageabweichungen—ein Werkzeug zur Bauteiltolerierung. Fortschritt-Berichte VDI Nr. 117 (1994)
Bohn, M.: Toleranzmanagement im Entwicklungsprozeß - Reduzierung der Auswirkungen von Toleranzen auf Zusammenbauten der Automobil-Karosserien. Dissertation, Universität Karlsruhe (1998)
Taguchi, G., Chowdhury, S., Taguchi, S.: Robust Engineering. McGraw-Hill, New York (2000)
Phadke, M.S.: Quality Engineering using Robust Design. Prentice Hall, Upper Saddle River (1989)
Dimitriou, P., Peng, Z., Lemon, D., Gao, B.: Diesel Engine Combustion Optimization for Bio-Diesel Blends Using Taguchi and ANOVA Statistical Methods, SAE Technical Paper 2013-24-0011 (2013)
Schuëller, G.I., Jensen, H.A.: Computational methods in optimization considering uncertainties – an overview. Comput. Methods Appl. Mech. Eng. 198, 2–13 (2008)
Ben-Tal, A., Nemirovski, A.: Robust optimization - methodology and applications. Math. Program. Ser. B 92, 453–480 (2002)
Beyer, H.G., Sendhoff, B.: Robust optimization - a comprehensive survey. Comput. Methods Appl. Mech. Eng. 196, 3190–3218 (2007)
Park, G.J., Lee, T.H., Lee, K.H., Hwang, K.H.: Robust design: an overview. Am. Inst. Aeronaut. Astronaut. 44, 181–191 (2006)
Trosset, M.W.: Taguchi and Robust Design, Technical report 96-31, Department of Computational and Applied Mathematics, Rice University (1997)
Hayer, C., Fiebig, S., Vietor, T., Sellschopp, J.: Robustheitsoptimierung innerhalb des Entwicklungs-prozesses durch Integration von Fertigungstoleranzen in die Simulation, 28. VDI-Fachtagung Technische Zuverlässigkeit (2017, to be published)
Wahid, Z., Nadir, N.: Improvement of one tactor at a time through design of experiments. World Appl. Sci. J. 21, 56–61 (2013). Mathematical Applications Engineering
Czado, C., Schmidt, T.: Mathematische Statistik. Springer, Heidelberg (2011)
Siebertz, K., van Debber, D., Hochkirchen, T.: Statistische Versuchsplanung – Design of Experiments (DoE). Springer, Heidelberg (2010)
Janon, A.: Analyse de sensibilité et réduction de dimension - Application à l’océanographie. Dissertation. Université de Grenoble (2006)
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S.: Global Sensitivity Analysis. The Primer. Wiley, Chichester (2008)
Viana, F.A.C.: Things you wanted to know about the Latin hypercube design and were afraid to ask. In: 10th World Congress on Structural and Multidisciplinary Optimization (2013)
Damblin, G., Couplet, M., Iooss, B.: Numerical studies of space-filling designs: optimization of Latin Hypercube Samples and subprojection properties. J. Simul. 7(4), 276–289 (2013)
Ranjan, P., Spencer, N.: Space-filling Latin hypercube designs based on randomization restrictions in factorial experiments. Stat. Probab. Lett. 94, 239–247 (2014)
Lazić, L.: Use of orthogonal arrays and design of experiments via taguchi methods in software testing. In: 18th International Conference on APPLIED MATHEMATICS (AMATH 2013), Budapest, Hungary (2013)
Kewlani, G., Crawford, J., Iagnemma, K.: A polynomial chaos approach to the analysis of vehicle dynamics under uncertainty. Veh. Syst. Dyn. 50(5) (2012)
Kalla, S.: Use of orthogonal arrays, quasi-monte carlo sampling, and kriging response models for reservoir simulation with many varying factors. Regional Engineering College Master Thesis, Warangal (2002)
Zein, S., Colson, B., Glineur, F.: An efficient sampling method for regression-based polynomial chaos expansion. Commun. Comput. Phys. 13, 1173–1188 (2013)
Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7, 308–313 (1965)
Bertsekas, D.P.: Constrained Optimization and Lagrange Multiplier Methods. Academic Press, London (1982)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Hayer, C., Fiebig, S., Vietor, T., Sellschopp, J. (2018). Optimization of Manufacturing Tolerances on Sheet Metal Components in the Development Process. In: Schumacher, A., Vietor, T., Fiebig, S., Bletzinger, KU., Maute, K. (eds) Advances in Structural and Multidisciplinary Optimization. WCSMO 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-67988-4_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-67988-4_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67987-7
Online ISBN: 978-3-319-67988-4
eBook Packages: EngineeringEngineering (R0)