Particle swarm optimization based algorithm for machining parameter optimization

Selection of machining parameters is an important step in process planning. In view of this problem, a new methodology based on particle swarm optimization (PSO) is developed to optimize machining conditions. First, by introducing the concept of history constraint satisfaction, constraint handling strategy suit for PSO optimization mechanism is presented. Furthermore, improvement is made by using direct search to intensify the local search ability of PSO algorithm. In addition, mathematical model for milling operation is established with respect to maximum production rate, subject to a set of practical machining constraints. The simulation results show that compared with genetic algorithm and simulated annealing, the proposed algorithm can improve the quality of the solution while speeding up the convergence process.