Flatness error is an important factor for effective evaluation of surface quality. The existing flatness error evaluation methods mainly evaluate the flatness error of a small number of data points on the micro scale surface measured by CMM, which cannot complete the flatness error evaluation of three-dimensional point cloud data on the micro/nano surface. To meet the needs of nano scale micro/nano surface flatness error evaluation, a minimum zone method on the basis of improved particle swarm optimization (PSO) algorithm is proposed. This method combines the principle of minimum zone method and hierarchical clustering method, improves the standard PSO algorithm, and can evaluate the flatness error of nano scale micro/nano surface image data point cloud scanned by atomic force microscope. The influence of the area size of micro/nano surface topography data on the flatness error evaluation results is analyzed. The flatness evaluation results and measurement uncertainty of minimum region method, standard least squares method, and standard PSO algorithm on the basis of the improved PSO algorithm are compared. Experiments show that the algorithm can stably evaluate the flatness error of micro/nano surface topography point cloud data, and the evaluation result of flatness error is more reliable and accurate than standard least squares method and standard PSO algorithm.

Steel strands are widely used in structures such as bridge cables, and their integrity is critical to keeping these structures safe. A steel strand is under the working condition of an alternating load for a long time, and fatigue damage is unavoidable. It is necessary to find characteristic parameters for evaluating fatigue damage. In this study, nonlinear coefficients and attenuation coefficients were employed to evaluate fatigue damage based on magnetostrictive guided wave testing. Unlike pipe and steel wire structures, there is a phenomenon of a notch frequency when guided waves propagate in steel strands. The influence of the notch frequency on the nonlinear coefficient and attenuation coefficient is discussed. The relationship between the nonlinear coefficient, attenuation coefficient, and cyclic loading times was obtained through experiments. The amplitudes of the nonlinear coefficient and attenuation coefficient both increased with the increase in cyclic loading times. The experiments also showed the effectiveness of using these two characteristic parameters to evaluate fatigue damage.

Aiming at the problems of complex control processes, strong model dependence, and difficult engineering application when the existing active suspension control strategy is applied to multi-axle vehicles, an active suspension control strategy based on the inverse position solution of a parallel mechanism is proposed. First, the active suspension of the three-axle emergency rescue vehicle is grouped and interconnected within the group, and it is equivalently constructed into a 3-DOF parallel mechanism. Then, the displacement of each equivalent suspension actuating hydraulic cylinder is calculated by using the method of the inverse position solution of a parallel mechanism, and then the equivalent actuating hydraulic cylinder is reversely driven according to the displacement, thereby realizing the effective control of the attitude of the vehicle body. To verify the effectiveness of the proposed control strategy, a three-axis vehicle experimental platform integrating active suspension and hydro-pneumatic suspension was built, and a pulse road experiment and gravel pavement experiment were carried out and compared with hydro-pneumatic suspension. Both types of road experimental results show that compared to hydro-pneumatic suspension, the active suspension control strategy based on the inverse position solution of a parallel mechanism proposed in this paper exhibits different degrees of advantages in reducing the peak values of the vehicle vertical displacement, pitch angle, and roll angle changes, as well as suppressing various vibration accelerations, significantly improving the vehicle’s driving smoothness and handling stability.

Aiming at the transformation and development trend of local colleges and universities in China, this paper takes the University of Huddersfield as an example to study the undergraduate education of applied technology-oriented universities in the UK. And it analyzes the teaching guiding ideology, teaching theory, educational idea and orientation, professional standards and certifications, curriculum and evaluation, student internships and employment, teaching and scientific research of teachers. All of these provide valuable reference for the transformation and development of local colleges and universities in China.

Some common problems, including the effect of non-detection regions on accuracy, the small size and multi-scale of defects,and the challenge of automatically optimizing neural network hyperparameters, are confronted during the metal gear end-face defect detection, lead to the inadequate performance of accuracy and efficiency, making them unsuitable for meeting the real-time online detection demands in industries. To address the problems above, this study proposes a method SF-YOLONet to detect defects on metal gear end faces by using the Optimized Evolutionary Algorithm. Firstly, a testing platform was constructed to detect surface defects on metal gear end-faces. Subsequently, in order to address the impact of non-detection regions on accuracy, this study introduces the SF algorithm, a visual saliency-based image extraction method, to eliminates interference between ineffective features in non-detection regions and edge burrs. Additionally, A network (YOLONet) for detecting end-face defects in metal gears is introduced, which integrates the CBAM module and BiFPN feature extraction strategy. These advancements enhance adaptive learning and feature extraction for small-sized defects on gear end-face, and combine low-resolution and deep-level semantic information, to detect small and multi-scale defects is enhanced. Finally, the ISSA algorithm is introduced to optimize the hyperparameters of the SF-YOLONet model, thereby avoiding the instability of manual parameterization. The experiment demonstrated that the SF-YOLONet model achieved an average precision of 98.01% and an F1 score of 0.99 on the Metal Gear end-face defect testing dataset. The average computation time for detection per image on the YOLONet model was 0.13 seconds. Compared to other deep learning models, the proposed SF-YOLONet model significantly improves precision and efficiency in detecting defects on gear end-face. This enhancement effectively meets the real-time online detection requirements of industries.