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.
Now a day's optimizing machining process parameters, various effective techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Artificial Bee Colony algorithm (ABC) have been used. In This paper we give an overview of PSO techniques to optimize milling machining process parameter of both traditional and modern machining from 2011 to 2018. Machining process parameters such as cutting speed, depth of cut and tool condition is mostly considered by researchers in order to minimize or maximize machining performances. From the review, the most machining process considered in PSO was multi-pass turning while the most considered machining performance was production costs.
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