The longitudinal–torsional ultrasonic vibration machining process is popular for machining hard and brittle materials. A longitudinal–torsional ultrasonic vibration system with diagonal slits was designed for the ultrasonic vibration machining process based on the principle of acoustics propagation in this study. Four uniform diagonal slits were cut off on the horn cylinder to transform the longitudinal ultrasonic vibration into a longitudinal–torsional composite motion. A finite element analysis was used to optimize the mechanical structure of the vibration system. Dynamic characteristics of the vibration system were numerically studied, and the relationship of diagonal slits versus the resonance frequency and amplitudes of longitudinal–torsional vibration was analyzed, so that the longitudinal–torsional output amplitude can be optimized by rational choice of structure parameters of diagonal slits. Moreover, the movement locus of the system output end was obtained by utilizing the Origin software, which was proved to be elliptical, and the accuracy of the finite element model was confirmed by comparing numerically and experimentally determined resonant characteristics. In addition, the verification of longitudinal–torsional ultrasonic vibration was proved for the designed ultrasonic vibration system.

Due to its superior characteristics, carbon fiber reinforced plastics (CFRP) have been widely used in aerospace and other fields. However, their effective and economical processing remains a challenge. The purpose of this paper was to experimentally investigate the influence of several machining parameters, namely spindle speed, cutting depth, feed rate, and grit size on different machining performances, i.e., grinding force, and surface roughness through longitudinal-torsional (L&T) ultrasonic vibration grinding CFRP. The response surface methodology with a Box—Behnken design was used to create the experiments. The fitting mathematical models of grinding force and surface roughness were established separately to explore the interaction between the machining parameters and the impact on machining results. With the purpose of improving the surface quality and reducing the grinding force, the NSGA-II was used for multi-objective optimization to obtain the Pareto optimal solution. Compared with the initial experimental parameters, the optimized results can significantly improve the surface roughness and reduce the cutting force.

Sliding electrical contacts need to be lubricated by conductive lubricants to perform low energy dissipation, high reliability, and long service life. This work studied the thermal stability, anti-corrosion capacity, and conductive, and tribological behaviors of several solid additives in multiply alkylated cyclopentanes (MACs), including carbon nanotubes (CNTs), multilayer graphene (MG), and silver microparticles. The results showed that all the additives possessed favorable thermal stability and corrosion resistance; in particular, CNTs and MG exhibited lower and more stable electrical contact resistance (ECR) and better lubricity abilities than Ag microparticles. Moreover, based on the characterization of the worn surfaces and the film thickness calculation, the favorable conductive and tribological properties of CNTs and MG were related to the high conductivity and specific structure of the additives and the good chemical inertness of MACs.

Tunnel disease detection and maintenance are critical tasks in urban engineering, and are essential for the safety and stability of urban transportation systems. Water stain detection presents unique challenges due to its variable morphology and scale, which leads to insufficient multiscale contextual information extraction and boundary information loss in complex environments. To address these challenges, this paper proposes a method called Deep Aggregation Network with Edge Information Supplement (DAEiS-Net) for detecting tunnel water stains. The proposed method employs a classic encoder–decoder architecture. Specifically, in the encoder part, a Deep Aggregation Module (DAM) is introduced to enhance feature representation capabilities. Additionally, a Multiscale Cross-Attention Module (MCAM) is proposed to suppress noise in the shallow features and enhance the texture information of the high-level features. Moreover, an Edge Information Supplement Module (EISM) is designed to mitigate semantic gaps across different stages of feature extraction, improving the extraction of water stain edge information. Furthermore, a Sub-Pixel Module (SPM) is proposed to fuse features at various scales, enhancing edge feature representation. Finally, we introduce the Tunnel Water Stain Dataset (TWS), specifically designed for tunnel water stain segmentation. Experimental results on the TWS dataset demonstrate that DAEiS-Net achieves state-of-the-art performance in tunnel water stain segmentation.

Bolts are critical components of tunnel linings, essential for ensuring safe tunnel operation. Due to their susceptibility to corrosion and detachment, it is vital to closely monitor them during maintenance. However, identifying a large number of corroded bolts presents challenges, particularly due to interference from complex background noise and the inherent limitations of CNNs in extracting local features. To address these issues, we introduces a novel multi-scale feature extraction detection network (MSDNet) designed to improve tunnel bolt maintenance by reducing false positives and missed detections. We incorporate a pyramid structure within the visual transformer framework to overcome CNNs’ limitations, enabling the generation of multi-scale corroded bolt feature maps that emphasize global information. Additionally, we develop a feature enhancement module (FEM) to capture more detailed features in small or localized areas during the search stage. Lastly, we design an efficient feature aggregation modules (FAM) to fuse coarse-level semantic corroded bolt information with fine-level features in a top-down pathway. We collected and organized a dataset of corroded bolts, and conducted comparative and ablation experiments to demonstrate the effectiveness of our proposed model. The results show that our model performs better in handling the challenges posed by this dataset.