Prediction of Dynamic Stiffness of Automotive Components Using Support Vector Methods

<div class="section abstract"><div class="htmlview paragraph">In the current scenario of EV revolution in the automotive industry, NVH performance of the vehicles is one of the major points of sale to the customers. Auxiliary components play one of the predominant roles in the contribution of noise to overall vehicle interior or exterior sound pressure levels, which impact customer vehicle comfort. CAE prediction of NVH performance of automotive components involves a lot of design iterative processes, large server space utilization, and time-consuming.</div><div class="htmlview paragraph">To reduce cost and time, data-driven technologies like AI algorithms can help CAE engineers because of their high efficiency and high precision. In the current research, a wiper motor mount stiffness prediction algorithm was designed based on the historical data using CAE analysis and AI algorithms, and improved prediction accuracy by tuning the parameters of AI algorithms using grid search methodology.</div><div class="htmlview paragraph">High prediction accuracy of wiper motor mount stiffness has been achieved with the method of support vector machine. CAE engineers can avoid iterative processes by utilizing the optimized design parameters from the prediction results without running full finite element analysis simulations.</div></div>