Abstract: This study compares the cutting performance of uncoated, as-deposited, and annealed AlCrSiWN-coated tools during dry machining of hardened steel. The uncoated tool failed within 8 minutes, exhibiting rapid wear (VBmax=300 μm) and thermal shock (5.2 °C/min). The as-deposited coating maintained steady wear (89.8-247.5 μm) for up to 60 minutes, but experienced abrupt wear acceleration to 569.7 μm at 90 minutes due to brittleness. The annealed coating extended wear life to 110 minutes with a final VBmax of 647.5 μm, demonstrating uniform wear progression and a 22% reduction in heating rate at 20 minutes. Temperature spikes (4.4-13.3°C/min) during severe wear correlated with expansion of the triangular wear band. Annealing effectively mitigated brittle fracture by facilitating plastic deformation, confirming its significant enhancement of coating durability under high-temperature cutting conditions. Read More

Abstract: As the core equipment of the ship power system, the operational reliability of the Marine engine is directly related to the safety and energy efficiency of ship navigation. With the increase in the complexity of Marine engineering systems and the growing demand for intelligence, the traditional fault diagnosis methods that rely on manual experience can no longer meet the needs of modern ship engineering. In recent years, breakthroughs in signal processing technology, artificial intelligence algorithms and multi-source data fusion technology have driven the innovation of Marine engine fault diagnosis technology. This paper systematically reviews the research progress and challenges in the field of Marine engine fault diagnosis from aspects such as the technological development history, core methods, experimental verification and future trends. Read More

Abstract: Damage detection in automotive components is of paramount importance for ensuring vehicle safety and performance. However, traditional detection methods suffer from significant limitations in both efficiency and accuracy. The recent advancements in deep learning and stereo vision technologies have introduced innovative approaches for intelligent damage detection in complex scenarios. This study proposes a damage detection and recognition system for automotive components that integrates stereo vision and the YOLOv5 deep learning algorithm. The research methodology includes constructing a stereo vision data acquisition platform, conducting image preprocessing and depth information extraction, applying various data augmentation techniques to enhance sample diversity, and leveraging transfer learning and hyperparameter optimization to improve model performance. Experimental results demonstrate that the system exhibits excellent performance in detection accuracy, real-time capability, and adaptability to small objects, effectively identifying diverse types of component damage. This research provides a reliable technological foundation for intelligent detection tasks in complex industrial scenarios, contributing significantly to improving quality control efficiency. Read More

Abstract: The rapid expansion of the electric vehicle (EV) industry has made damage recognition and evaluation a critical issue. Traditional manual damage assessment methods are inefficient and susceptible to subjective bias, making it challenging to meet the increasing demand for precise and efficient damage evaluation in the EV repair and insurance sectors. To address this challenge, this study proposes a machine vision-based damage recognition and evaluation system for electric vehicles. The system integrates image processing, deep learning algorithms, and attention mechanisms to autonomously identify damage types and quantify their severity. By utilizing high-resolution image acquisition, deep learning feature extraction, damage classification, and regression analysis, the system not only enhances the efficiency of damage recognition but also improves the accuracy of damage assessment. Performance evaluation results indicate that the system performs stably and adaptively across various environments and vehicle types, effectively handling complex damage scenarios in electric vehicles. The novelty of this research lies in its application of machine vision and deep learning techniques to automate the damage evaluation process, filling a gap in the field and providing a smart and efficient solution for the EV industry. Read More

Abstract: This study systematically analyzed the special requirements of the Australian market for vehicle adaptability. Firstly, the unique climate environment of Australia (extreme high temperatures, sandstorms, high humidity), road traffic characteristics (narrow lanes, complex signs, frequent roundabouts), and user usage habits (widespread use of trailers, CarPlay dependence, etc.) were elaborated in detail. Secondly, by comparing the differences between China and Australia in environmental testing standards, traffic regulations, and user needs, the shortcomings of the existing testing system in China in dealing with UV aging, dynamic speed limit sign recognition, and right-hand drive human-machine interaction were revealed. Finally, targeted development suggestions were proposed, including verification of special operating conditions (roundabout+ speed bump), optimization of charging compatibility, and cooperation with local operators for adaptive development. The research provides a systematic testing and verification method reference for car companies to enter the Australian market. Read More

Abstract: The perceived quality of the whole vehicle is a core issue that car companies are currently focusing on, and dimensional engineering is a key factor affecting its quality. The interior and exterior trims of a car will produce slight deformations due to the influence of ambient temperature, causing changes in the matching dimensions of the interior trims, and placing higher requirements on the consistency of the quality of automobile products. This article analyzes the deformation mechanism of typical parts at different temperatures, combines dimensional engineering technology, reveals the influence of temperature on the gaps of the interior trims of the whole vehicle, and measures the dynamic changes of the gaps and face differences of the test pieces during temperature changes through actual tests, providing a theoretical basis for the dimensional engineering design of automotive interior and exterior trims, while providing practical experience references for product design, development and quality improvement, helping to predict problems in the early stages of design, and creating products with higher quality standards through collaboration in all aspects of production and manufacturing. Read More