In the automated production line for suspended insulators, precise alignment of the U-shaped notch in iron caps is crucial for effective gluing. This study introduces a system based on machine vision that automates the alignment process. The system initially preprocesses the images of iron caps to segment the U-shaped contour. It utilizes the method of quadratic maximum contour connectivity domain to accurately identify the target U-shaped region. The alignment process involves calculating the coordinates of the largest external rectangle's longest edge and the external circle's center point. These coordinates are instrumental in determining the necessary rotation angle for proper notch alignment. The fixture then adjusts the iron cap based on this calculated angle, ensuring precise alignment. Experimental validations of this system have demonstrated a notch alignment error within 0.5 degrees with 96.51% accuracy and an error within 1 degree with 100% accuracy. The algorithm's execution time is a swift 0.034 seconds. Both the error margins and operational speed satisfy the stringent requirements of the automatic production line.

Limitations inherent in conventional rule generation methodologies, particularly concerning knowledge redundancy and efficiency in product design, are addressed through the adoption of a rough set-based approach in this study. An enhancement to the Ant Colony Optimization (ACO) algorithm's information gain ratio is introduced by integrating a redundancy detection mechanism, which notably accelerates the convergence process. Furthermore, the application of a classification consistency algorithm effectively minimizes the number of attributes, facilitating the extraction of potential associative rules. Comparative validation performed on a public dataset demonstrates that the proposed attribute reduction approach surpasses existing methods in terms of attribute count reduction, reduction rate, and execution time. When applied to an automotive design case study, the approach yields rules with 100% coverage and accuracy, characterized by a reduced average number of attributes per rule. These findings underscore the superiority of the rough set-based methodology in generating product design rules, providing a robust framework that enhances both the precision and applicability of the design process.

This investigation addresses the issue of premature failure or damage to bearing components in aeroengines, which often results from the release of dissolved gases in the lubricant due to environmental pressure changes during operation. Employing the three-dimensional Reynolds equation and focusing on an ideal lubricating oil, a lubrication model for the engine camshaft's oil film was developed. The formation and extent of gaseous voids within plain bearings were analyzed. The study systematically explored how fit clearance and lubricating oil viscosity influence oil film pressure and thickness. It was found that a reduced fit gap increases the oil film pressure gradient while decreasing the film's thickness. Additionally, although variations in lubricating oil viscosity do not affect the distribution of oil film thickness, they significantly impact the pressure exerted on the oil film, with higher viscosities leading to increased pressures. These findings provide essential theoretical guidance for the safety assessment of aeroengine plain bearings.

The study investigates the efficacy of eco-friendly cooling fluids, specifically vegetable oil and water mixtures, in the machining of EN19 steel, with a focus on enhancing performance metrics while promoting environmental sustainability. Machining parameters, including cutting speed, feed rate, and depth of cut (DOC), were analyzed for their effects on surface roughness, tool temperature, cutting forces, and material removal rate (MRR). The study employed a hybrid optimization approach, integrating Taguchi's orthogonal array (OA) method with grey relational analysis (GRA), to evaluate the effectiveness of these eco-friendly cutting fluids. The analysis revealed that spindle speed significantly influenced the MRR, while the DOC notably affected cutting force and tool temperature. The choice of coolant was found to have a considerable impact on surface roughness. Although the Taguchi method effectively optimized individual machining parameters, GRA provided a more comprehensive evaluation by synthesizing multiple performance metrics into a single index, achieving an accuracy of 80.17%, which surpassed the 72.44% accuracy of the Taguchi method. These findings underscore the potential of GRA to optimize the machining process of EN19 steel, offering substantial improvements in manufacturing efficiency and sustainability. The study highlights the importance of adopting eco-friendly practices in industrial machining, demonstrating that the integration of GRA and Taguchi methods can lead to more sustainable and efficient manufacturing processes.