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Application of multibody simulation for semitrailer optimization

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EN
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EN
This paper presents an approach of optimization of a truck semitrailer suspension system, with utilization of multibody model; its purpose was to find the best values of operational parameters: stiffness and damping factors, in order to minimize the disadvantageous influence of force distribution in the high risk areas, where preceding strength analysis has pointed out dangerous load values. The model contains elements of two different types: flexible and rigid bodies, in purpose of increasing the accuracy level of conducted numerical calculations. A number of simulations with different parameters and under different load cases have been carried out, combined with a parametric and structural sensitivity analysis, what has enabled an estimation of individual factors influencing particular forces that have been the objectives of optimization procedure. The stiffness and damping coefficients of the construction suspension system have been adjusted by applying metamodeling techniques. Basing on the chosen design of experiment results, this procedure allows for an approximation of the behaviour of the analysed construction in the whole design space. In this process, two different approaches have been used: Kriging and polynomial regression, and both have been compared to the simulations results. Finally, using a desirability function, the most optimal solution has been found.
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  • AGH University of Science and Technology Department of Robotics and Mechatronics A. Mickiewicza Av. 30,30-059 Krakow, Poland, korta@agh.edu.pl
Bibliografia
  • [1] Uhl, T., Computer aided manufacturing and design, WNT, Warsaw 1997.
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  • [3] Gallina A., Response Surface Methodology as a tool for analysis for uncertainty in structural dynamics, Ph.D. Thesis, AGH, University of Science and Technology, 2009.
  • [4] Myers, R. H., Montgomery, D. C., Response Surface Methodology: Process and Product Optimization Using Designed Experiments (second edition), John Wiley & Sons, New York, NY 2002.
  • [5] Wang, G. G., Shan, S., Review of Metamodeling Techniques in Support of Engineering Design Optimization, ASME Transactions, Journal of Mechanical Design, 2006.
  • [6] Jin, R., Chen, W., Simpson, T. W., Comparative studies of metamodeling techniques under multiple modeling criteria, Structural and Multidisciplinary Optimization, Vol. 23, pp. 1 13, 2001.
  • [7] Derringer, G., A Balancing Act: Optimizing a Product’s Properties, Quality Progress 27(6), pp. 51-58, 1994.
  • [8] Derringer, G., Suich, R., Simultaneous Optimization of Several Response Variables, Journal of Quality Technology 12, pp.214-219, 1980.
  • [9] Castillo, Del E., Montgomery, D. C., McCarville, D. R., Modified Desirability Functions for Multiple Response Optimization, Journal of Quality Technology, Vol. 28, No. 3, 1996.
  • [10] Kim, K. J., Lin, D. K. J., Simultaneous optimization of mechanical properties of steel maximizing exponential desirability functions, Journal of the Royal Statistical Society: Series C, Vol. 49, Is. 3, pp. 311–325, 2000.
  • [11] Simpson, T. W., Lin, D. K. J., Chen, W., Sampling Strategies for Computer Experiments: Design and Analysis, International Journal of Reliability and Applications, 2001.
  • [12] Simpson, T. W., Comparison of Response Surface and Kriging Models in the Multidisciplinary Design of an Aerospike Nozzle, ICASE Report No. 98-16, 1998.
  • [13] Amago, T., Sizing Optimization Using Response Surface Method in FOA, R&D Review of Toyota CRDL, Vol. 37, No. 1, 2002.
  • [14] Simpson, T. W., Peplinski, J. D., Koch, P. N., Allen, J. K., On the Use Of Statistics In Design and the Implications for Deterministic Computer Experiments, Proceedings of DETC’ 97, Sep. 14 -17, Sacramento, California 1997.
  • [15] Harrington, E., The Desirability Function, Industrial Qual. Control, pp. 494–498, 1965.
  • [16] Heltona, J. C., Davisb, F. J., Johnson, J. D., A comparison of uncertainty and sensitivity analysis results obtained with random and Latin hypercube sampling, Reliability Engineering and System Safety 89, p. 305–330, 2005.
  • [17] Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S., Global Sensitivity Analysis. The Primer, John Wiley & Sons, Ltd, Chichester, UK 2008.
  • [18] Agrawal, Om P., Shabana, A. A., Dynamic analysis of multibody systems using components models, Computers & Structures, Vol. 21, Is. 6, pp. 1303–1312, 1985.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ8-0018-0058
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