Optimization of Milling Conditions by Using Particle Swarm Optimization Technique: A Review

Abstract

Abstract-a review of publications associated with the optimization of milling conditions by particle swarm analysis method. Milling is the most common form of machining, a material removal process, which can create a variety of features on a part by cutting away the unwanted material In order to optimize the cutting conditions, the empirical relationships between input and output variables should be established in order to predict the output. Optimization of these predictive models helps us to select appropriate input variables for achieving the best output performance. In this review paper the study is covered regarding the optimization of different input parameters and results are analyzed.

Chatter detection in milling process based on the combination of wavelet packet transform and PSO-SVM

“…Even though genetic algorithm (GA) and particle swarm optimization(PSO) have good advantage of parameter optimization of SVM in terms of classification recognition, PSO is simple to operate and reduce the computation time significantly with respect to GA [30,31]. Wang et al [32] proposed a hybrid chatter detection method is for chatter classification in end milling, and found that this approach can recognize the stable, transition, and chatter states more accurately than the other traditional approaches by PSO optimizing the input parameters of SVM.…”