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<div class="section abstract"><div class="htmlview paragraph">Experimental modal analysis (EMA) is a measurement technique to assess the dynamical properties of mechanical components and systems in various phases of their life cycle, e.g. for design, end-of-line testing and health monitoring. The most common EMA uses accelerometers, which provide high frequency acceleration measurements at a few discrete locations. However, attached accelerometers may alter the systems mass and damping properties and multiple tests are required to obtain spatially dense information. To overcome these issues, in this paper we use high-speed cameras and video processing algorithms. In fact, cameras as contact-less sensors do not modify the dynamics of the system under test. Furthermore, cameras provide full-field displacement data, allowing to obtain spatially dense transfer functions with a single excitation, which reduces the experiment duration. On the downside, camera measurements are suitable for relatively low-frequency applications only and require optical contrast on the component surface. While previous camera based research was focused on flat, plate-like components, we demonstrate the methodology on a 3D automotive coil spring. We use a stereo vision setup to measure the 3D displacement field, employing Lucas-Kanade optical flow as feature tracker. Thereby, we make use of local averaging for noise reduction. As cameras are able to capture static information the geometry of the component is obtained in addition to the modal parameters. This allows for intuitive visualization of the EMA results. For the automotive coil spring under investigation we obtain the displacement field up to 140 <i>Hz</i> with an estimated displacement accuracy in the range of a few micrometer. The EMA results are compared to an accelerometer based EMA highlighting the advantages of camera based EMA. Furthermore, we investigate the sensitivity of the camera based EMA with respect to excitation and environmental conditions and discuss two alternative markers to enhance image contrast.</div></div>

Engineering design is traditionally performed by hand: an expert makes design proposals based on past experience, and these proposals are then tested for compliance with certain target specifications. Testing for compliance is performed first by computer simulation using what is called a discipline model. Such a model can be implemented by a finite element analysis, multibody systems approach, etc. Designs passing this simulation are then considered for physical prototyping. The overall process may take months, and is a significant cost in practice. We have developed a Bayesian optimization system for partially automating this process by directly optimizing compliance with the target specification with respect to the design parameters. The proposed method is a general framework for computing a generalized inverse of a high-dimensional non-linear function that does not require e.g. gradient information, which is often unavailable from discipline models. We furthermore develop a two-tier convergence criterion based on (i) convergence to a solution optimally satisfying all specified design criteria, or (ii) convergence to a minimum-norm solution. We demonstrate the proposed approach on a vehicle chassis design problem motivated by an industry setting using a state-of-the-art commercial discipline model. We show that the proposed approach is general, scalable, and efficient, and that the novel convergence criteria can be implemented straightforwardly based on existing concepts and subroutines in popular Bayesian optimization software packages.