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The tensile stress-strain curves of iron and a variety of steels, covering a wide range of strength level, over a wide strain rate range on the order of 10⁻³ ~ 10³s⁻¹, were obtained systematically by using the Sensing Block Type High Speed Material Testing System (SBTS, Saginomiya). Through intensive analysis of these results, the strain rate sensitivity of the flow stress for the large strain region, including the viscous term at high strain rates, the true fracture strength and the true fracture strain were cleared for the material group of the ferrous metals. These systematical data may be useful to develop a practical constitutive model for computer codes, including a fracture criterion for simulations of the dynamic behavior in crash worthiness studies and of work-pieces subjected to dynamic plastic working for a wide strain rate range.

This study is focused on the fatigue properties of automobile high-strength bolts, including the effect of mean stress level, pre-processing schedule and the residual stresses. And the mean stress levels are 0.3, 0.5 and 0.7 times to the tensile strength (σ_B) of the material respectively. The main results obtained are as follows: 1) the fatigue strength increases under the mean stress loading, but the differences between the loading levels are not so evident; 2) most of the cases in this study are broken from the bottom of the screw thread, and the crack initiated from the impurities.

Image defect detection of casting parts is a key part of the production process in the machinery manufacturing industry. The traditional methods are ineffective because traditional computer image processing methods require a large number of manual features to be set artificially, and the detection time is too long. In order to save human resources and improve the efficiency of image defect detection, this paper proposes a deep learning-based defect detection method for automobile parts. This paper selects EfficientNetB0 as the backbone framework of the target detection network, which significantly reduces the memory usage of the model and shortens the model inference time, while improving the model detection accuracy. Facing the problem of small samples of defect image dataset, we analyze the image characteristics of the dataset and introduce shape transformation and scale scaling as the basic online data enhancement method according to the industrial field image projection law. Then, it is expected to combine the traditional image processing algorithms according to the characteristics of casting parts with different depth distribution and multiple morphological changes, and develop a special image defect data enhancement method. This further improves the performance of the model and increases the detection accuracy of the algorithm by 22.3% without increasing the data.

Automobile architecture is a crucial phase in the process of design. The model assessment process in the hybrid vehicle modeling assessment becomes more in-depth, and designers exchange ideas. The critical aspect of automotive modeling is that various models have varying views on vehicle styling. The evaluation process is long, complicated, and efficient in automotive simulation. The evaluation process takes time. Therefore, in the paper, an Automobile Modelling Optimization Design Based on Virtual Reality Technology (AMOD-VRT) is proposed to improve automobile modeling and design research to be more interactive. Computational Design Method is used to create and design the entire vehicle as well as its surrounding environment. An Integrated Virtual Analysis can enclose the models mentioned above and create a virtual environment in which the user can interact to design a model virtually by themselves. The paper provides complete virtual design modeling of an automobile, allowing the user to work more efficiently based on the results. By applying virtual reality technology in the automobile design phase, interactive and network-based remote research on automobile modeling can make the automobile design progression more appropriate, easier to communicate with designers and decrease automobile design’s development cost and cycle. The simulation analysis is performed based on safety, performance, and efficiency, proving the reliability of the proposed framework. Various parameters obtain the simulation results on automobile design as the automobile vehicle design performance ratio is 87.16%, increasing the visual effects in AMOD ratio is 88.77%, improving visual effects with optimized design ratio is 84.5%, interaction and collaboration of automobile modeling ratio are 82.26% and reduce vehicles accident using virtual reality ratio is 93.80%. The computer-based virtual reality technology in the automotive industry can effectively accelerate new product development in the future.

This research studies optimization design of the thickness of sound packages for a passenger car. The major characteristics indexes for performance determined to evaluate the process are sound pressure level of the interior middle frequency noise and weight of the sound package. Three kinds of materials of sound packages are selected for the optimization process. The corresponding parameters of the sound packages are the thickness of the insulation plate for outer side of the firewall, thickness of the sound absorbing wool for inner side of the firewall, thickness of PU foam for the front floor, and thickness of PU foam for the rear floor, respectively. In this paper, the optimization procedure is a multi-objective optimization. Therefore, gray relational analysis (GRA) is applied to decide the optimal combination of sound package parameters. Furthermore, the principal component analysis (PCA) is used to calculate the weighting values which are corresponding to multiple performance characteristics. Then, the results of the confirmation tests uncover that GRA coupled with principal analysis methods can effectively be applied to find the optimal combination of the thickness of the sound packages at different positions for a passenger car. Thus, the proposed method can be a useful tool to improve the automotive interior middle frequency noise and lower the weight of the sound packages. Additionally, it will also be useful for automotive manufactures and designers in other fields.

The aim of this paper is to provide an overview of the existing industrial practices used for cabin noise control in various industries such as automotive, marine, aerospace, and defense. However, emphasis is placed on automobiles and armored vehicles. Generally, automobile cabins usually constitute of thin structural panels, where the fundamental frequency typically falls below 200Hz. If a specific structural mode couples with a specific acoustic mode of the cabin, booming noise occurs. As such, discomfort may be felt by the occupants. Fundamentally, vibroacoustics problems may be minimized if the acoustic modes and the structural modes are decoupled, which is achieved usually by structural modifications or acoustical treatments. However, if excessively performed, the weight limitation of an automobile design will be exceeded; not to mention the adverse effect of increased weight on several factors such as fuel efficiency, mileage life of tires and acceleration of the vehicle. Moreover, current solutions have several drawbacks in low frequency noise control. In light of this, it is of great interest to explore the feasibility of acoustic metamaterials as an alternative with hope to improve cabin noise.

China’s automotive industry has been dedicated to a series of carbon-reduction efforts and has strived for comprehensive green and low-carbon transformation in order to achieve carbon peaking and carbon neutrality goals. On the basis of automobiles’ whole life-cycle (WLC) carbon emission accounting, this paper calculates life-cycle carbon emissions per vehicle, proposes green and low-carbon development path for China’s automotive industry, quantifies and analyzes implicated carbon-reduction potential, and puts forth suggestions for high-quality green and low-carbon development of China’s automotive industry. The first is to establish a sound standard and data management system; the second is to promote research, development and application of low-carbon materials and low-carbon technologies; the third is to accelerate the pace of fostering a new consumption model in the automotive industry. The research results can further support national policy-making regarding carbon emissions, promote corporations’ research, development and application of low-carbon technologies, encourage green and low-carbon consumption, and lead the automotive industry to achieve WLC neutrality.

To validate the manufacturing feasibility of a novel Press Hardening Steel (PHS 1800), the B pillar reinforced panel was trail-produced by hot forming at temperature of 875 °C and 900°C. Besides, resistance spot welding was also carried out at welding current of 6-10 kA. Microstructure and mechanical properties of the B-pillar were examined using tensile test and scanning electron microscope (SEM). The results showed that the sample after hot formed at 900 °C exhibited the better mechanical properties, the tensile strength exceeded 1800 MPa; meanwhile, the elongation reached up to 8.6% which is comparative to conventional 1500 MPa counterparts. Under the fixed electron pressure and duration, the optimal welding current was determined to be 8.0-9.0 kA.

This study is focused on the fatigue properties of automobile high-strength bolts, including the effect of mean stress level, pre-processing schedule and the residual stresses. And the mean stress levels are 0.3, 0.5 and 0.7 times to the tensile strength (σ_B) of the material respectively. The main results obtained are as follows: 1) the fatigue strength increases under the mean stress loading, but the differences between the loading levels are not so evident; 2) most of the cases in this study are broken from the bottom of the screw thread, and the crack initiated from the impurities.