This study has analyzed the effect of noise sources of power seat slide adjuster on sound quality and human sensitivity through statistical methods. First, sound quality analysis using sound quality metrics was performed to analyze objective data. Next, the subjective evaluation of sound quality was performed by a jury test. There were two types of sound sources used for the jury test; one was two original sounds measured in the operating test and the other one was eight virtual sound sources that were produced by amplifying a specific frequency of original sounds. It was designed to derive the causal relationship between each noise source and human sensitivity. Thirdly, we analyzed the correlation between the sound quality metrics and the sound pressure level of the noise source through the factor analysis. As a result, four independent variables were derived. Lastly, stepwise regression analysis was performed using four independent variables and the results of the jury test. The derived regression models had considerable explanatory power. From this, it was possible to understand the influence of the noise source of the seat slide adjuster on the sound quality and human sensitivity.

This study investigated the squeal mechanism induced by friction in a lead screw system. The dynamic instability in the friction noise model of the lead screw was derived through a complex eigenvalue analysis via a finite element model. A two degree of freedom model was described to analyze the closed solutions generated in the lead screw, and the friction noise sensitivity was examined. The analysis showed that the main source of friction noise in the lead screw was the bending mode pair, and friction-induced instability occurred when the ratio of the stiffness of the bending pair modes was 0.9–1. We also built an architecture to predict multiple outputs from a single model using deep neural networks and demonstrated that friction-induced instability can be predicted by deep neural networks. In particular, instability with nonlinearity was predicted very accurately by deep neural networks with a maximum absolute difference of about 0.035.