Cutting parameters play an essential role in the economics of machining. In this paper, particle swarm optimization (PSO), a novel optimization algorithm for cutting parameters optimization (CPO), was discussed comprehensively. First, the fundamental principle of PSO was introduced; then, the algorithm for PSO application in cutting parameters optimization was developed; thirdly, cutting experiments without and with optimized cutting parameters were conducted to demonstrate the effectiveness of optimization, respectively. The results show that the machining process was improved obviously.

Due to cutting parameters playing an important role in machining economics and machining qualities, much attention has been paid to select optimum cutting parameters. In this paper, cutting parameters optimization for constant cutting force was discussed based-on virtual machining comprehensively. Particle swarm optimization (PSO) was used to seek for the optimal spindle speed and feed rate. The framework of virtual machining based cutting parameters optimization was established. Then two controlled experiments were conducted to demonstrate the effectiveness of cutting parameters optimization both with physical cutting and computer simulation. The results of experiments show that machining process with constant cutting force can be achieved via cutting parameters optimization based on virtual machining.

Traditional adaptive control technologies in machining process optimization are limited in applications because they depend much on sensors, controllers and other hardware. An off-line optimization method for end milling process with constant cutting power is presented. On taking advantage of virtual machining which simulates milling process, acquires cutting parameters and predicts cutting forces, method taking constant cutting power as an objective is discussed to optimize feed rates and cutting speeds. Based on optimal result, the feed rates and spindle revolutions in NC program are re-scheduled. Controlled milling experiments show that machining time is reduced and machining stability is improved by using the optimized NC program.