The translation of motion lift, as an important performance metric of a reversing six-link loader working device, is influenced by multiple factors, such as the mechanical structure, system components, and operational experience. To ensure that the loader’s motion lift performance is optimized, this paper takes the fork trajectory and the horizontal angle between the bucket cylinder and the ground as the main optimization objectives. Kinematic modeling and multi-objective optimization are conducted to reduce the influence of external factors on the motion lift process. Firstly, a parametric model of the reversing six-link mechanism is established based on its geometric and symmetric characteristics, and the expressions for the fork’s motion trajectory and the cylinder–ground angle are derived. Then, an optimization model is constructed with the aim of minimizing both the translational error during fork lifting and the horizontal angle of the bucket cylinder. An improved multi-objective genetic algorithm is employed for the global search and optimization. Inspired by the principle of symmetry, the algorithm incorporates a structured search strategy that enhances convergence efficiency and solution balance. A multi-criteria decision function is further applied to identify the optimal solution from the Pareto front. Finally, a real-vehicle experiment validates the optimization results. The findings confirm that the proposed method significantly improves the translational performance of the fork and effectively controls the horizontal angle of the cylinder while also enhancing the driver’s visibility and coordination of the entire system. These results provide a theoretical and engineering basis for the symmetry-informed multi-objective performance optimization of loader working devices.

In the rolling process of heavy cylinders, the deformation section is subjected to the effects of compression and shear. In order to analyze the influences of the shear effect on the microstructure evolution characteristics, a mathematical model was established and the rolling process was simulated. Firstly, shear-compression specimen (SCS) and ordinary cylinder specimens were designed, high-temperature compression experiments were carried out and the mathematical model of microstructure evolution considering shear effect was established; then, a program based on finite element software was developed to simulate the microstructure evolution process, and the feasibility of the development program was verified by compression experiments. Finally, a macro–micro coupling model based on the development program was established to simulate the microstructure evolution of the heavy cylinder during the rolling process. Then, the influence of the shear effect on the microstructure evolution was analyzed. The results showed that the shear effect had a great influence on the heavy cylinder. Dynamic recrystallization was more likely to occur in the heavy cylinder during the rolling process and the grain refinement was more obvious; compared with the case without considering the shear effect, the volume fraction of dynamic recrystallization was increased by 0.25%, and the grain size was refined by 30 μm.

Fault diagnosis in axial piston pumps is key to ensuring the proper operation of a hydraulic system. Slipper wear, as a typical fault in piston pumps, is challenging to accurately diagnose because the faults are very similar for different forms and degrees of wear. The achievement of accurate fault diagnosis of different forms and degrees of wear in the slipper will greatly improve the reliability of axial piston pump operation and, at the same time, provide new ideas for research into similar fault diagnosis problems in other rotating machinery. Therefore, a method of fault diagnosis based on the following symmetric dot pattern (SDP) and multi-channel densely connected convolutional networks (DenseNet) is proposed in this paper. The method applies an SDP transformation to transform the slipper failure signal into an SDP image, which achieves the fusion of triaxial vibration signals and enriches the signal features. The inception module is improved by replacing the original structure with larger convolutional kernels in multiple branches and decomposing the larger convolutional kernels. The inception module, the convolutional block attention module (CBAM), and the DropBlock method are introduced into DenseNet to improve feature extraction capability, computational efficiency, and model generalization ability. Experiments are performed on several slipper wear fault SDP image datasets, and all the indices produced by the proposed method are higher than those of the traditional convolutional neural networks, which fully proves the effectiveness and superiority of the procedure.

This paper addresses the issue of energy waste in servo pump-controlled hydraulic presses during regenerative conditions, where a significant amount of energy is dissipated by braking resistors. A novel energy-saving system based on a common DC bus and supercapacitor energy storage is proposed. Through system design and mathematical modeling, the key parameters of the energy storage system are analyzed. To tackle the energy management challenge under complex working conditions, a global energy regulation method is introduced, incorporating a Model Predictive Control power-following strategy and a Finite State Machine state-switching strategy. Experimental results demonstrate that, under typical stretching conditions, the overall energy-saving efficiency of the system exceeds 37%, significantly enhancing the equipment’s energy performance. The findings of this study confirm that the proposed scheme offers excellent energy-saving effects and strong engineering feasibility.