Abstract
In this research, experimental vibration data in time and frequency domains are used to identify material properties in the finite element model of a car seat frame using a surrogate-based multi-objective genetic algorithm. The frame consists of several components made from different steel grades. In the first step, the Young modulus, the density, and the Poisson’s ratio of different components of the frame are identified by optimizing two objective functions based on comparing the experimental and numerical natural frequencies and mode shapes. The response surface optimization using different surrogate models and different numbers of vibration modes are performed and the best-compromised material properties are reported. In the second step of the material identification procedure, the damping coefficients of the frame’s components are estimated by comparing the experimental impulse responses with those of the FE model using the MOGA. Based on the results, the presented approach can be efficiently applied to practical problems to estimate their material properties with reasonable computational costs.
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Fatahi, L. Vibration-based material properties identification of a car seat frame in time and frequency domains using multi-objective genetic algorithm. Struct Multidisc Optim 65, 22 (2022). https://doi.org/10.1007/s00158-021-03136-2
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DOI: https://doi.org/10.1007/s00158-021-03136-2