%0 Journal Article
%T Particle swarm intelligence based optimisation of high speed end-milling
%A F. Cus
%A U. Zuperl
%J Archives of Computational Materials Science and Surface Engineering
%D 2009
%I 
%X Purpose: Selection of machining parameters is an important step in process planning therefore a new evolutionary computation technique is developed to optimize machining process. This study has presented multi-objective optimization of milling process by using neural network modelling and Particle swarm optimization. Particle Swarm Optimization (PSO) is used to efficiently optimize machining parameters simultaneously in high-speed milling processes where multiple conflicting objectives are present. The goal of optimization is to determine the objective function maximum (predicted cutting force surface) by consideration of cutting constraints.Design/methodology/approach: First, an Artificial Neural Network (ANN) predictive model is used to predict cutting forces during machining and then PSO algorithm is used to obtain optimum cutting speed and feed rates.Findings: During optimization the particles ¡®fly¡¯ intelligently in the solution space and search for optimal cutting conditions according to the strategies of the PSO algorithm. The simulation results show that compared with genetic algorithms (GA) and simulated annealing (SA), the proposed algorithm can improve the quality of the solution while speeding up the convergence process.Research limitations/implications: The experimental results show that the MRR is improved by 28%. Machining time reductions of up to 20% are observed.Practical implications: While a lot of evolutionary computation techniques have been developed for combinatorial optimization problems, PSO has been basically developed for continuous optimization problem. PSO can be an efficient optimization tool for solving nonlinear continuous optimization problems, combinatorial optimization problems, and mixed-integer nonlinear optimization problem.Originality/value: An algorithm for PSO is developed and used to robustly and efficiently find the optimum machining conditions in end-milling. This paper opens the door for a new class of EC based optimization techniques in the area of machining. This paper also presents fundamentals of PSO optimization techniques.
%K Machining
%K End-milling
%K Particle Swarm Optimization
%U http://www.archicmsse.org/vol09_3/0933.pdf