Abstract
Standard development process of a product involves several CAE and CAD analyses in order to determine parameter values satisfying technical product specifications. In case of nonlinear behavior of the system, the computational time may quickly increase. In the current study, a new methodology that integrates Neural Networks (NN) and Genetic Algorithms (AG) is introduced to analyse virtual models. The proposed tool is based on different computational , mathematical and experimental methods that are combined together to distil a single tool that permits to evaluate in a few seconds how behaviors of certain product vary when any design parameter is altered. As example, the methodology is applied to adjust design parameters of an exhaust system, showing same accuracy range than FEA, but strongly reducing the simulation time.
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© 2012 Springer-Verlag Berlin Heidelberg
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Bernardini, A., Asensio, J., Olazagoitia, J.L., Biera, J. (2012). Evolutionary Neural Networks for Product Design Tasks. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_38
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DOI: https://doi.org/10.1007/978-3-642-28942-2_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28941-5
Online ISBN: 978-3-642-28942-2
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