Most existing vision-based roughness measurements primarily rely on statistical information from grayscale images or intensity information from color images. However, the structural information of images has not been fully and effectively utilized. To more accurately measure the roughness of grinding surfaces, a visual measurement method of grinding surface roughness based on aliasing region index and neural network is proposed. Firstly, color images of grinding surface are obtained under red and green illumination. Secondly, aliasing regions of red and green images are extracted through fuzzy clustering segmentation and morphological processing. Then the aliasing width and the aliasing dispersion of aliasing region can be calculated as indices for roughness measurement. Thirdly, the relationship model between aliasing region index and grinding surface roughness is constructed using the back propagation (BP) neural network. The results demonstrate that the aliasing dispersion index has a better correlation with grinding surface roughness than the aliasing width index. The method based on the aliasing dispersion index and BP neural network is feasible and accurate for grinding surface roughness measurement.

A directional valve is a core component of the electro-hydraulic shakers in fatigue testing machines, controlling the cylinder or motor that drives the piston for reciprocating linear or rotary motion. In this article, a high-speed rotating directional valve with a symmetrical flow channel layout is designed to optimize the force on the valve core of the directional valve. A comparative analysis is conducted on the flow capacity of valve ports with different shapes. A steady-state hydrodynamic mathematical model is established for the valve core, the theoretical analysis results of which are verified through a Computational Fluid Dynamics (CFD) simulation of the fluid domain inside the directional valve. A prototype of the rotatory directional valve is designed and manufactured, and an experimental platform is built to measure the hydraulic force acting on the valve core to verify the performance of the valve. The displacement curves at different commutation frequencies are also obtained. The experimental results show that the symmetrical flow channel layout can significantly optimize the hydraulic force during the movement of the valve core. Under a pressure of 1 MPa, the hydraulic cylinder driven by the prototype can achieve a sinusoidal curve output with a maximum frequency of 60 Hz and an amplitude of 2.5 mm. The innovation of this design lies in the creation of a directional valve with a symmetric flow channel layout. The feasibility of the design is verified through modeling, simulation, and experimentation, and it significantly optimizes the hydraulic forces acting on the spool. It provides us with the possibility to further improve the switching frequency of hydraulic valves and has important value in engineering applications.

Efficient acquisition of rock mass parameters is a critical step for conducting numerical simulations in tunneling and ensuring the safety of subsequent construction. This paper proposes an intelligent back-analysis method for key rock mass parameters (Young’s modulus, Poisson’s ratio, cohesion, and friction angle) based on excavation-induced deformation data, using a deformation database that incorporates multi-feature values from tunnel excavation. This study employs five machine learning algorithms with single-feature inputs and three deep neural networks (DNNs) with multi-feature inputs, with a particular focus on convolutional neural network (CNN) due to their superior performance in terms of both accuracy and efficiency. The results demonstrate that the CNN model incorporating excavation features achieves excellent performance in parameter back-analysis, with an R² of 0.99 and 97.8% of predictions having errors within 5%. Compared with machine learning models using single-feature inputs, the CNN-based approach improves predictive performance by an average of 13.9%. Furthermore, compared with other DNNs, the CNN consistently outperforms across various evaluation metrics. This study also investigates the CNN’s capability to predict rock mass parameters using deformation data from early-stage excavation. After ten excavation steps, 96.9% of test samples had prediction errors within 5%. Finally, the proposed method was validated using field-monitored deformation data from a real highway tunnel project, confirming the method’s effectiveness and practical applicability.