Corrosion behavior is critical to the application of lightweight aluminum/steel joints using new resistance spot welding (RSW) technology. The study investigated the corrosion mechanism and the shear strength of RSW joints comprising 1.2 mm 5182 aluminum and 1.5 mm DP780 galvanized steel. Electrochemical corrosion tests were conducted on the base materials and various positions of the welds in a 3.5% NaCl solution. This result revealed that the corrosion susceptibility of the interfacial intermetallic compound (IMC) layer was not accelerated by the aluminum nugget because of the noble corrosion potential. Subsequently, the spray acceleration test was employed to investigate the corrosion mechanism. It is noteworthy that microcracks, as well as regions enriched with silicon and oxygen at the interface front, are preferential to corrosion during salt spray exposure, instead of the IMC layer. Moreover, the shear strength of the joints decreases with the reduction in the effective joint area after the salt spray exposure of the weld joints. This research systematically explored the corrosion behavior and its relationship with the mechanical properties of Al alloy/steel RSW joints.

The high-pressure die-casting process can effectively manufacture aluminium alloy castings with complex shapes and thin wall thicknesses. However, due to the complex flow characteristics of the liquid metal during the mould-filling process, there are significant differences in the mechanical properties of different parts of the casting. This paper analyses the effect of the high-speed ram transition position on porosity and mechanical properties of Al-Si-Mn-Mg aluminium alloys in the high-pressure die-casting (HPDC) process, comparing the 1160 mm and 1200 mm positions. Using a comprehensive methodology that combines CT, tensile tests, and SEM, the research demonstrates that the 1160 mm position improves mechanical properties and reduces porosity, with a larger gap at the near-end of the casting, where the yield limit and elongation of the casting increased by 13% and 25% at 1160 mm compared to 1200 mm, respectively. This result shows that appropriate adjustment of the high-speed ram transition position can effectively optimise the organisational structure of thin-walled castings, and then improve their mechanical properties.

X-ray inspection is a crucial technique for identifying defects in castings, capable of revealing minute internal flaws such as pores and inclusions. However, traditional methods rely on the subjective judgment of experts, are time-consuming, and prone to errors, which negatively impact the efficiency and accuracy of inspections. Therefore, the development of an automated defect detection model is of significant importance for enhancing the scientific rigor and precision of casting inspections. In this study, we propose a deep learning model specifically designed for detecting small-scale defects in castings. The model employs an end-to-end network architecture and features a loss function based on the Wasserstein distance, which is tailored to optimize the training process for small defect targets, thereby improving detection accuracy. Additionally, we have innovatively developed a dual-layer Encoder-Decoder multi-scale feature extraction architecture, BiSDE, based on the Hadamard product, aimed at enhancing the model’s ability to recognize and locate small targets. To evaluate the performance of the proposed model, we conducted a series of experiments, including comparative tests with current state-of-the-art object detection models such as Yolov9, FasterNet, Yolov8, and Detr, as well as ablation studies on the model’s components. The results demonstrate that our model achieves at least a 5.3% improvement in Mean Average Precision (MAP) over the existing state-of-the-art models. Furthermore, the inclusion of each component significantly enhanced the overall performance of the model. In conclusion, our research not only validates the effectiveness of the proposed small-scale defect detection model in improving detection precision but also offers broad prospects for the automation and intelligent development of industrial defect inspection.

In this study, the quasi-static and dynamic compressive mechanical behavior of a rolled Fe-28Mn-10Al-1.2C steel (low-density) was investigated. X-ray diffraction, optical microscopy, electron backscattered diffraction and transmission electron microscopy were conducted to characterize the microstructure evolution. The results displayed that the steel has remarkable strain rate sensitivity and strong strain hardenability under high strain rate compression. Most specifically, the deformation behavior was changed with the increase in the strain rate. A feasible mathematical analysis for the calculation of stacking fault energies and the critical resolve shear stresses for twinning was employed and discussed the nucleation of the twinning. The microband-induced plasticity and twinning-induced plasticity controlled the deformation under high strain rate compression and provided a strong strain hardening effect. The higher mechanical response can increase the broad use of low-density steel in automobile applications.

The fracture performance and damage prediction of die-cast materials are critical to guarantee the safe application of die-cast structural components in lightweight vehicles. Monotonic loading experiments were conducted on different shapes of die-cast aluminum alloy A356 specimens. Finite element simulation models of the A356 monotonic loading experiments were established, and the stress state of the specimens during the loading process was analyzed. The Modified Mohr–Coulomb (MMC) failure model of A356 was fitted by the failure strain under different stress states. Finally, the established MMC failure model was verified by a uniaxial compression experiment and bending experiment. The results show that the MMC failure model can be applied to the prediction of the fracture behavior of A356.