Abstract
Abstract The automotive and aerospace industries are increasingly using light-weight material to improve vehicle performance while reducing fuel consumption. However, using light-weight material can increase the air-borne and structure-borne noise. Thus, special attention needs to be paid in designing the vehicle body structure and evaluating its dynamics. In order to accomplish this, modal analysis is conventionally used. In this technique, the structure is excited using an impulse hammer or a mechanical shaker, and the response is measured using accelerometers. However, using contact-based transducers can mass-load the structure and can only provide data at a few discrete points. In the last decade, stereo-photogrammetry and three-dimensional digital-image correlation have received special attention in collecting operating data for structural analysis. These non-contact optical techniques provide a wealth of distributed data over the entire structure. However, the stereo-camera system is limited by the field of views of the cameras and can only measure the response on the parts of a structure for which cameras have lines of sight. Therefore, a single pair of DIC cameras may not be able to provide deformation data for an entire structure. A multi-view 3D DIC approach, however, can be used to predict the vibrational characteristics of a full vehicle. A pair of DIC cameras is passed over the entire vehicle to capture the deformation data of each field of view. The measured data includes the geometry and displacement data, which is mapped into a global coordinate system using 3D transformation matrices. The obtained data in the time domain for each field of view is transformed to the frequency domain using a Fast Fourier Transformation (FFT) to extract the operational deflection shapes and resonant frequencies for each field of view. The obtained deflection shapes are scaled and stitched in the frequency domain to extract the operating deflection shapes of the entire vehicle.
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