Prediction of Trimmed BIW Noise transfer function based on BIW response using Machine Learning

This research focuses on the prediction of the Noise Transfer Function (NTF) in a trimmed Body-in-White (BIW) structure by leveraging machine learning technique considering BIW-level NTF, dynamic stiffness at engine mount attachment points, and Vibration Transfer Function (VTF). The study employs an iterative methodology, adjusting the thickness of various BIW panels to analyse their impact on noise transfer characteristics. Frequency response functions are generated at the BIW attachment points to assess the effectiveness of these modifications. By systematically increasing and decreasing panel thicknesses, and stiffness at the attachment point and trimmed BIW level responses the study aims to optimize the acoustic performance of the BIW structure. The findings provide critical insights into the relationship between structural modifications and noise transfer, offering valuable guidance for automotive design and engineering during early design stage to reduce the development time.