The cyclic impact load of cutting force is one of the main factors that lead to the insert failure during the milling process. According to the asymmetrical heavy-duty milling process of water chamber head, the simulation milling experiments using actual machining parameters are conducted under laboratory conditions, and the change rule of milling force with cutting parameters is analyzed. Moreover, the simulation of interrupted cutting is carried out to study the cycle changes of cutting force and stress distribution of insert during the insert cutting-in and cutting-out workpiece. The dynamic cutting force model is established by considering the irregularity of the cutting area of circular insert and the variation of asymmetric milling thickness. Based on the analysis of comparison between the calculated value of the model and the experimental value, the cutting force model can describe the dynamic change of cutting force in milling process. It is of great significance to analyze and master the changing trend of dynamic cutting force for the study of insert failure and the formulation of reasonable processing parameters in milling process.
The reptile search algorithm (RSA) is a swarm-based metaheuristic algorithm inspired by the encirclement and hunt mechanisms of crocodiles. Compared with other algorithms, RSA is competitive but still suffers from low population diversity, unbalanced exploitation and exploration, and the tendency to fall into local optima. To overcome these shortcomings, a modified variant of RSA, named MRSA, is proposed in this paper. First, an adaptive chaotic reverse learning strategy is employed to enhance the population diversity. Second, an elite alternative pooling strategy is proposed to balance exploitation and exploration. Finally, a shifted distribution estimation strategy is used to correct the evolutionary direction and improve the algorithm performance. Subsequently, the superiority of MRSA is verified using 23 benchmark functions, IEEE CEC2017 benchmark functions, and robot path planning problems. The Friedman test, the Wilcoxon signed-rank test, and simulation results show that the proposed MRSA outperforms other comparative algorithms in terms of convergence accuracy, convergence speed, and stability.
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