Aiming at the conflict between the defect feature recognition capability and the detection speed of current vision inspection techniques in the task of detecting tiny defects in printed circuit boards (PCBs). In this paper, we proposed an EPD-YOLO with a focus information transfer (FIT) structure and a structurally flexible head (SFHead). While improving the network’s ability to recognize tiny defects in similar PCBs through FIT, the feature information capturing capability of the dual SFHead structure is utilized to ensure detection accuracy and improve real-time detection speed. Experimental results show that the proposed EPD-YOLO has a mAP of 97.6 % , while the number of network parameters is only 5.13M, and it takes only 7.4 ms to detect an image, which achieves a better balance between accuracy and speed.

The multi-scale characteristics of wall roughness are the main factors inducing particle deposition in pneumatic conveying bend pipes. However, existing simplified models cannot effectively characterize the complex features of rough walls. To elucidate the mechanism by which wall roughness regulates the deposition behavior of particles of different sizes, this study constructs a 90° bend pipe model with multi-scale random roughness characteristics based on the three-dimensional Weierstrass–Mandelbrot [three-dimensional (3D) W–M] function. By integrating the Reynolds stress model, the discrete phase model, and a critical deposition velocity model, the deposition characteristics of 1–100 μm sepiolite particles were systematically investigated. The results indicate that near-wall recirculation vortices and secondary flows are the main factors driving differences in deposition hotspots for particles of varying sizes. The particle-size screening mechanism is identified as the main reason for the enhanced deposition of particles with Stokes number (St) < 1 on rough walls. Significant differences in deposition behavior among particles of different sizes arise from the competition between adhesion-collision mechanisms. This study systematically reveals the physical mechanisms by which wall roughness regulates multi-size particle deposition, providing a theoretical basis for optimizing pneumatic conveying pipe design and reducing system energy consumption.

The classifier is an essential tool for the development of contemporary engineering technology. The application of classifiers is to categorize mixed-sized particles into multi-stage uniform particle sizes. In current studies, the particles in the classifier obtain their initial velocity when feeding. The classification effect is impacted by the inability to precisely control the initial state of the particles. To solve this problem, a pusher feed classifier was designed in this study, and a numerical simulation was performed to investigate its flow field characteristics and classification performance using the RNG-DPM method. A pusher is utilized to achieve particle feeding without initial velocity and to precisely control the initial state of the particles in the classification flow field. A newly developed two-way air inlet structure is designed to provide a superimposed flow field and enable the five-stage classification. Our results show that this pusher feed classifier has the best classification effect when the vertical airflow velocity is 10 m/s and the horizontal airflow velocity is 3 m/s. Meanwhile, the classification size ratio (CSR) from outlet 1 to outlet 5 was 1.24, 0.55, 0.45, 0.39, and 0.15, respectively.

The conical pick is an essential component of roadheaders used for cutting rock. During the rock-breaking process, these picks interact with the rock, resulting in self-rotation, which enhances the wear uniformity of conical picks, thereby prolonging their service life. Since the phenomenon of self-rotation is generated passively by random contact forces with the rock surface, it is challenging to quantitatively measure the extent of self-rotatory speed. In order to investigate the correlation between the self-rotatory speed of conical picks and wear, this article establishes various self-rotatory speeds for vertical rock-breaking wear experiments involving conical picks. It analyzes the relationship between quantitative parameters, such as the equivalent stress and wear, through simulation. The results of the study indicate that the optimal self-rotatory speed of the conical pick is 16 rpm when it is rotated vertically to break the rock, resulting in minimal wear. When the equivalent stress and Mohr–Coulomb safety factor are optimized, it is essential to consider the changes in normal force and the variation in the area affected by the safety factor. This leads to an increase in wear as the cutting distance increases, indicating that a higher self-rotatory speed does not necessarily improve the wear performance of conical picks.