Based on the resistance characteristics, a model of theoretical digging force was proposed in this paper, taking the tangential force, the normal force, and the bending moment into account simultaneously. Utilizing the relation among the normal resistance, the resistance moment, and the tangential resistance in practical digging process, three independent unknown quantities are transformed into only one variable. Afterwards, according to different digging patterns and complete machine limiting conditions, this research derived the constraint inequalities of the limiting digging force (LDF) and established the calculation models for LDF. Then, based on the value distribution laws of the digging resistance coefficient and the resistance moment coefficient, the calculation process and corresponding method of LDF under a given digging posture were obtained. Taking the digging resistance obtained by testing for 35 t hydraulic excavator with backhoe attachment as the reference, this paper compared the calculation results of the theoretical digging force for complete machine with those of the LDF model proposed in this research. The comparative results indicate that the LDF is consistent with the fact that the theoretical digging force is larger than or at least equal to the actual digging resistance. So, the LDF can exactly show the real limiting digging ability of the excavator more accurately. In this way, it can provide basis for mechanism optimization, structural strength design, trajectory planning, and control automation of the excavator.

In order to obtain the optimal cladding process parameters for repairing the inner wall of the cylinder, 316L stainless steel powder was laser clad onto 27SiMn steel, which is the base material of the inner wall of the cylinder. The CCD (Central Combination Design) experiment scheme was designed by the response surface method. A surrogate model between input variables (laser power, scanning speed, and powder-feeding speed) and response values (intactness, dilution rate, and the micro-hardness of the cladding layer) was established. The adaptive chaotic differential evolution algorithm (ACDE) was used to optimize the process parameters and the optimization results were verified by experiments. The results show that the optimum parameters are a laser power of 1350 w, a scanning speed of 11.7 mm/s, and a powder-feeding rate of 2.5 g/min. After cladding, the macroscopic quality of the cladding layer was increased by 11.1%, the micro-hardness was increased by 7.1%, and the dilution rate was reduced by 24.7%. During the friction wear experiments, it was found that the maximum wear depth of the optimal specimen was 149.72 μm, which was smaller and more wear-resistant than the specimen in the control group. The results provide theoretical data for the repair and strengthening of the inner wall of the hydraulic support cylinder.

High-speed laser cladding shows significant potential for application in the field of high-performance surface hardening due to its low heat input and high cladding efficiency. However, the pool solidification time is significantly reduced at high scanning speeds, resulting in a narrower process window and making it more difficult to ensure coating formation stability and control performance. Therefore, this study employed high-speed laser cladding technology to prepare FeCrNiB alloy coatings, and systematically conducted research on process parameter optimization and friction properties. Firstly, the response surface method (RSM) was used to establish quantitative relationship models between laser power, scanning speed, and powder feed rate and the dilution ratio, forming coefficient, and microhardness. Then, the hybrid differential evolution and NSGA-II algorithm (DE-NSGA-II) was employed for multi-objective optimization. Finally, a systematic analysis was conducted on the friction and wear properties of the coatings produced under the optimal process parameters. The results indicate that the interaction between laser power and scanning speed has a significant impact on the dilution behavior of the coating, while the coupling between scanning speed and powder feed rate governs the formation characteristics and microhardness evolution of the coating. The experiment verified that the prediction error for the optimal parameters is controlled within 5%, demonstrating good engineering applicability. Further analysis indicates that grain refinement and the formation of strengthening phases in the optimal coating are the key mechanisms behind the significant improvement in hardness and wear resistance, and the coating primarily exhibits a mild abrasive wear mechanism. This work realizes the multi-objective optimization of the high-speed laser cladding process via RSM and DE-NSGA-II algorithm, which provides a novel and efficient method for parameter optimization and engineering application of high-speed laser cladding.