© Copyright by International OCSCO World Press. All rights reserved. 2007 VOLUME 22 ISSUE 2 June 2007 Short paper 5 of Achievements in Materials and Manufacturing Engineering of Achievements in Materials and Manufacturing Engineering High speed end-milling optimisation using Particle Swarm Intelligence F. Cus a , U. Zuperl a, *, V. Gecevska b a Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia b Faculty of Mechanical Engineering, University in Skopje, PO Box 464, 1000 Skopje, Macedonia * Corresponding author: E-mail address: uros.zuperl@uni-mb.si Received 20.03.2007; published in revised form 01.06.2007 Analysis and modelling AbstrAct Purpose: In this paper, Particle Swarm Optimization (PSO), which is a recently developed evolutionary technique, is used to efficiently optimize machining parameters simultaneously in high-speed milling processes where multiple conflicting objectives are present. Design/methodology/approach: Selection of machining parameters is an important step in process planning therefore a new methodology based on PSO is developed to optimize machining conditions. Artificial neural network simulation model (ANN) for milling operation is established with respect to maximum production rate, subject to a set of practical machining constraints. An ANN predictive model is used to predict cutting forces during machining and PSO algorithm is used to obtain optimum cutting speed and feed rate. Findings: 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. PSO is proved to be an efficient optimization algorithm. Research limitations/implications: Machining time reductions of up to 30% are observed. In addition, the new technique is found to be efficient and robust. Practical implications: The results showed that integrated system of neural networks and swarm intelligence is an effective method for solving multi-objective optimization problems. The high accuracy of results within a wide range of machining parameters indicates that the system can be practically applied in industry. Originality/value: An algorithm for PSO is developed and used to robustly and efficiently find the optimum machining conditions in end-milling. The new computational technique has several advantages and benefits and is suitable for use combined with ANN based models where no explicit relation between inputs and outputs is available. This research opens the door for a new class of optimization techniques which are based on Evolution Computation in the area of machining. Keywords: Machining; End-milling; Particle Swarm Optimization 1. Introduction Increasing productivity, decreasing costs, and maintaining high product quality at the same time are the main challenges manufacturers face today. The proper selection of machining parameters is an important step towards meeting these goals and thus gaining a competitive advantage in the market [1]. Many researchers have studied the effects of optimal selection of machining parameters of end milling [2]. It can be formulated and solved as a multiple objective optimization problem [3]. In practice, efficient operation of milling operation requires the simultaneous 1. Introduction