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Technical Paper

Multi-Objective Optimization of Interior Noise of an Automotive Body Based on Different Surrogate Models and NSGA-II

2018-04-03
2018-01-0146
This paper studies a multi-objective optimization design of interior noise for an automotive body. An acoustic-structure coupled model with materials and properties was established to predict the interior noise based on a passenger car. Moreover, three kinds of approximation models related damping thickness and the root mean square of the driver’s ear sound pressure level were established through Latin hypercube method and the corresponding experiments. The prediction accuracy was analyzed and compared for the approximate response surface model, Kriging model and Radial Basis Function neural network model. On this basis, multi-objective optimization of the vehicle interior noise was conducted by using NSGA-II. According to the optimization results, the damping composite structure was applied on the car body structure. Then, the comparison of sound pressure level response at driver’s ear location before and after optimization was performed at speed of 60 km/h on a smooth road.
Journal Article

Objective Evaluation of Interior Sound Quality in Passenger Cars Using Artificial Neural Networks

2013-04-08
2013-01-1704
In this research, the interior noise of a passenger car was measured, and the sound quality metrics including sound pressure level, loudness, sharpness, and roughness were calculated. An artificial neural network was designed to successfully apply on automotive interior noise as well as numerous different fields of technology which aim to overcome difficulties of experimentations and save cost, time and workforce. Sound pressure level, loudness, sharpness, and roughness were estimated by using the artificial neural network designed by using the experiment values. The predicted values and experiment results are compared. The comparison results show that the realized artificial intelligence model is an appropriate model to estimate the sound quality of the automotive interior noise. The reliability value is calculated as 0.9995 by using statistical analysis.
Journal Article

Prediction of the Sound Absorption Performance of Polymer Wool by Using Artificial Neural Networks Model

2014-04-01
2014-01-0889
This paper proposes a new method of predicting the sound absorption performance of polymer wool using artificial neural networks (ANN) model. Some important parameters of the proposed model have been adjusted to best fit the non-linear relationship between the input data and output data. What's more, the commonly used multiple non-linear regression model is built to compare with ANN model in this study. Measurements of the sound absorption coefficient of polymer wool based on transfer function method are also performed to determine the sound absorption performance according to GB/T18696. 2-2002 and ISO10534- 2: 1998 (E) standards. It is founded that predictions of the new model are in good agreement with the experiment results.
Technical Paper

Sound Absorption Optimization of Porous Materials Using BP Neural Network and Genetic Algorithm

2016-04-05
2016-01-0472
In recent years, the interior noise of automobile has been becoming a significant problem. In order to reduce the noise, porous materials have been widely applied in automobile manufacturing. In this study, the simulation method and optimal analysis are used to determine the optimum sound absorption of polyurethane foam. The experimental simulation is processed based on the Johnson-Allard model. In the model, the foam adheres to a hard wall. The incident wave is plane wave. The function of the model is to calculate the noise reduction coefficient of polyurethane foam with different thickness, density and porosity. The back propagation neural network coupled with genetic optimization technique is utilized to predict the optimum sound absorption. A developed back propagation neural network model is trained and tested by the simulation data.
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