Abstract
This research work presents fatigue life evaluation techniques for an automotive vehicle aluminum front subframe using virtual test simulation technology with nonlinear suspension components model. The technology was used for improving the accuracy of the polynomial model used in conventional analysis. The proposed nonlinear suspension components models were developed using direct approach. The effects of the nonlinear elements on the prediction of the fatigue life were also analyzed. Actual aluminum front subframe was tested using half-car road test simulator to verify the accuracy of the models. It was found that the proposed nonlinear models yield more accurate results than conventional polynomial models. The proposed virtual test simulation technology with nonlinear suspension components model can be used to predict fatigue life for vehicle chassis structures more accurately.
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ADAMS (2008). ADAMS User’s Manual. MSC Software Corporation.
Barber, A. (2000). Accurate models for complex vehicle components using empirical methods. SAE Paper No. 2000-01-1625.
Billings, S. A. (1999). A direct approach to identification of nonlinear differential models from discrete data. Mechanical System and Signal Processing, 13, 739–755.
Cho, J. U. and Han, M. S. (2010). Durability and stress analysis on automotive lower arm. Trans. KSMTE 19, 3, 376–380.
Choi, S. J., Park, J. W., Jeon, K., Yoo, Y. M., Choi, G. J. and Park, T. W. (2006). A study on durability performance estimation for development of chassis corner module. Trans. KSAE 14, 1, 159–166.
Durali, M. and Kassaiezadeh, A. (2001). A neural network approximation of nonlinear car model using adams simulation results. SAE Paper No. 2001-01-3324.
Fe-Fatigue (2007). Fe-Fatigue User’s Manual. nCode Int. Ltd.
Leser, C., Renner, T. and Salmon, D. (2002). Accurate shock absorber load modeling in an all terrain vehicle using black box neural network techniques. SAE Paper No. 2002-01-0581.
Lim, J. Y., Kang, W. J., Kim, D. S. and Kim, G. H. (2007). Effect of the flexibility of automotive suspension components in multibody dynamics simulations. Int. J. Automotive Technology 8, 6, 745–752.
MATLAB (2010). MATLAB User’s Manual. The MathWorks, Inc.
NASTRAN (2010). NASTRAN User’s Manual. MSC Software Corporation.
Saraf, M. R., Jambhale, M. S., Pawar, P. R., Shaikh, A., Dwivedi, S., Boone, F., Garcia, J. and Stachel, T. (2011). Integration of real and virtual tools for suspension development. SAE Paper No. 2011-26-0115.
Son, S. H., Ryu, Y. G., Heo, S. J., Lee, Y. H. and Kim, D. S. (2007). Calculation of suspension component forces for durability analysis using virtual testing lab. Conf. Proc. KSAE, 646–651.
Woo, J. W., Han, I. S. and Lee, S. B. (2010). A study on field test for bellows in the vehicle exhaust system. Conf. Proc. KSAE, 2355–2358.
Yang, F., Hamilton, J. and Cheng, H. (2008). Virtual rig simulation in the exhaust system development. SAE Paper No. 2008-01-1215.
You, S. and Joo, S. G. (2006). Virtual testing and correlation with spindle coupled full vehicle testing system. SAE Paper No. 2006-01-0993.
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Nam, J.S., Shin, H.W. & Choi, G.J. Durability prediction for automobile aluminum front subframe using nonlinear models in virtual test simulations. Int.J Automot. Technol. 15, 593–601 (2014). https://doi.org/10.1007/s12239-014-0062-2
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DOI: https://doi.org/10.1007/s12239-014-0062-2