Automotive Science and Engineering

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Parametric design optimization of an automotive body crashworthiness improvement is presented. The thicknesses of parts are employed as design variables for optimization whose objective is to increase the maximum deceleration value of the vehicle center of gravity during an impact. Using the Taguchi method, this study analyzes the optimum conditions for design objectives and the impact factors and their optimal levels are obtained by a range analysis of the experiment results. A full frontal impact is implemented for the crashworthiness simulation in the nonlinear dynamic code, LS-DYNA. The controllable factors used in this study consist of the six inside foreheads structural parts, while design parameters are relevant thicknesses. The most interestingly the maximum deceleration of the vehicle center of gravity is reduced by 20% during a full frontal impact while several parts experience mass reduction.

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Today, the utilization of lithium-ion batteries (LIBs) has significantly increased as an energy storage technology. In recent years, the high demand for lithium for LIB has resulted in a significant increase in the consumption of lithium-containing materials. It is anticipated that the reduction of lithium due to the limited reserves of lithium will be one of the major challenges in the future. The primary component of the lithium-ion battery industry is lithium, which is extracted from natural minerals and saline water. However, the extraction of lithium from natural minerals and saline water is a complex process that requires a significant amount of energy. Conversely, the quantity of batteries that are approaching the end of their lifespan is unavoidably increasing at an alarming rate. In order to address the obstacles that the lithium battery supply chain encounters, it is imperative that a variety of recycling technologies and methodologies be further developed. This article concentrates on technologies that can recycle lithium compounds from LIB through distinct processes and procedures. These stages are further divided into two pre-treatment phases and a lithium extraction stage. The lithium extraction stage is further divided into three primary methods: pyrometallurgy, hydrometallurgy, Direct. This review article quantitatively compares and analyzes the processes, advantages, disadvantages, efficiency, price, environmental contamination, and degree of commercialization of each recycling method. This review can offer a suitable perspective to enhance this path.

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In metal casting, detecting defects like pores and cracks in X-ray images is crucial for product quality and safety. This study presents an advanced U-Net architecture for semantic segmentation of defects in the GDXray dataset, achieving superior accuracy. By formulating defect detection as an inverse problem reconstructing material density from X-ray projections the method integrates transfer learning, data augmentation, and Convolutional Block Attention Modules (CBAM) to address low contrast-to-noise ratios and limited data. Pretrained on synthetic Radon transform projections, the U-Net, enhanced with CBAM, sharpens focus on defect regions, improving boundary precision by 5%. Data augmentation, including rotations, flips, and noise injection, generates 5,000 synthetic images to overcome data scarcity. Experiments on 2,727 grayscale GDXray images demonstrate a mean Intersection over :union: (mIoU) of 0.85, a 15% improvement over baseline U-Net models, with 97.8% accuracy for pores and 94.5% for cracks. The inverse problem approach reduces false negatives by 12%, excelling in noisy conditions. Compared to methods like Mask R-CNN, this approach advances non-destructive evaluation (NDE) for casting applications, ensuring reliability and safety. Validated on laboratory X-ray data, the model offers a scalable solution for industrial defect detection. Future work will optimize computational efficiency and explore multi-modal data to enhance robustness.