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The requirement for low emissions and better vehicle performance has led to the demand for lightweight vehicle structures. Two new lightweight methods of design and optimization for the lower control arm were proposed in this research to improve the effectiveness of the traditional lightweight method. Prior to the two lightweight design and optimization methods, the static performance, including strength, stiffness and mode, and fatigue performance for the lower control arm were analyzed and they provided constraints for subsequent design and optimization. The first method of lightweight design and optimization was integrated application of topography optimization, size optimization, shape optimization and free shape optimization for the control arm. Topography optimization was first applied to find the optimal distribution form of reinforcement rib for the lower control arm. Size optimization was then applied in this study to optimize the plate thickness.

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.

Most of the skin injuries caused by traffic accidents, sports, falls, etc. are in the intermediate strain rate range (1-100s-1), and the injuries may occur at different sites, impact velocities, and orientations. To investigate the multifactorial mechanical properties of rat skin at intermediate strain rates, a three-factor, three-level experimental protocol was established using the standard orthogonal table L9(34), which includes site (upper dorsal, lower dorsal, and ventral side), strain rate (1s-1, 10s-1, and 100 s-1), and sampling orientation (0°, 45°, and 90° relative to the spine). Uniaxial tensile tests were performed on rat skin samples according to the protocol to obtain stress-stretch ratio curves. Failure strain energy was selected as the index, and the influence of each factor on these indexes, the differences between levels of each factor, and the influence of errors on the results were quantified by analysis of variance (ANOVA).

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.

Artificial intelligence systems are highly accepted as a technology to offer an alternative way to tackle complex and non-linear problems. They can learn from data, and they are able to handle noisy and incomplete data. Once trained, they can perform prediction and generalization at high speed. The aim of the present study is to propose a novel approach utilizing the adaptive neuro-fuzzy inference system (ANFIS) and the fuzzy clustering method for automotive ride performance estimation. This study investigated the relationship between the automotive ride performance and relative parameters including speed, spring stiffness, damper coefficients, ratios of sprung and unsprung mass. A Takagi-Sugeno fuzzy inference system associated with artificial neuro network was employed. The C-mean fuzzy clustering method was used for grouping the data and identifying membership functions.

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.

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.

The hippocampus plays a crucial role in brain function and is one of the important areas of concern in closed head injury. Hippocampal injury is related to a variety of factors including the strength of mechanical load, animal age, and helmet material. To investigate the order of these factors on hippocampal injury, a three-factor, three-level experimental protocol was established using the L9(34) orthogonal table. A closed head injury experiment regarding impact strength (0.3MPa, 0.5MPa, 0.7MPa), rat age (eight- week-old, ten-week-old, twelve-week-old), and helmet material (steel, plastic, rubber) were achieved by striking the rat's head with a pneumatic-driven impactor. The number of hippocampal CA3 cells was used as an evaluation indicator. The contribution of factors to the indicators and the confidence level were obtained by analysis of variance.

In this paper, tensile experiments were performed on the dorsal and ventral skin of rats, and the mechanical properties of the skin in these two sites were compared and analyzed. A three-factor experimental protocol of site (dorsal and ventral), strain rate (0.71s-1, 7.1×10-3s-1), and sampling orientation (0°, 45° and 90° relative to the spine) was established for tensile test using the L6(31×22) orthogonal table modified from the standard orthogonal table L4 (23). Uniaxial tensile experiments were performed on rat skin samples to calculate the stress-strain curve. The failure strain energy was selected as the index, and the sum of squared deviations of the factors to the index was calculated by analysis of variance (ANOVA), and the contributions of the factors to the failure strain energy were evaluated. The results showed that the site factor has the largest effect on the tensile strain energy with a contribution of 88.9% and a confidence level of 95%.