Simulation Prediction And Optimization Analysis Of Vehicles Door Closing Sound Quality

The sound of a car closing has complex non-linear characteristics.The evaluation of door-closing sound quality mainly relies on experienced engineers to listen to the sound signals,then score them one by one.More importantly,the optimization of the door-closing sound quality needs to be carried out a fter the production of the prototype car.The flexibility of this method is poor,and the whole optimization process is tedious.To deal with the problems of door-closing sound quality evaluation and optimization,this paper takes a car company’s door as the research object.The prediction model and simulation method are applied to predict the door-closing sound quality before the vehicles are put into production.By optimizing the structure of the door seal strip to improve the sound quality of the door c losure.Connecting the sound quality and structure.The process of building sound quality by modifying structure and material parameters is explored.The main contents of this paper are as follows:Firstly,to collect the signal of door closing.Evaluating the sound quality of door closure with paired comparison method.Multiple linear regression and BP neural network model are proposed to predict the door-closing sound quality based on the objective psychoacoustic parameters.The multi-linear regression model and BP neural network prediction model are compared comprehensively.It’s decided to use the neural network model to predict the sound quality of the simulated sound signal.Secondly,the finite element and transient boundary element mod els of the door are established.The acceleration characteristics of each component are analyzed by finite element simulation.Taking acceleration of door components as boundary condition,the sound pressure time curve of door is simulated by transient boundary element method.We release the vibration and noise experiment of door closing.The experimental results are in good agreement with the simulation results.The loudness and sharpness of the simulated and experimental sound signals are extracted and input into the established BP neural network model.The error between simulation and experimental prediction is less than 10%.Finally,the accelerat ion of the door panel is adjusted by optimizing the structure of the door seal strip.Three kinds of door sealing strips w ith different cross sections are selected to analyze the influence of the car door acceleration and energy absorption.Choosing No.3 seal strip with the best cushioning effect to optimize door-closing sound quality.The predicted score of door-closing sound quality after optimization is 36 points higher than before.The results show that the double cushion sealing strip can effectively reduce the loudness and sharpness and improve the door-closing sound quality.