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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 is suitable for use combined with ANN based models where no explicit relation between imputs 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.
Wydawca
Rocznik
Tom
Strony
75--78
Opis fizyczny
Bibliogr. 14 poz., rys.
Twórcy
autor
autor
autor
- Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia, uros.zuperl@uni-mb.si
Bibliografia
- [1] U. Zuperl, F. Cus, M. Milfelner, Fuzzy control strategy for an adaptive force control in end-milling, Journal of Materials Processing Technology 164 (2005) 1472-1478.
- [2] J. Kopac, Influence of high speed cutting on the structure of machined high speed steel material, The Eleventh Scientific Conference on Contemporary Achievements in Mechanics, Manufacturing and Materials Science CAM3S'2005, Gliwice-Zakopane, 2005, 40-44.
- [3] F. Cus, U. Zuperl, E. Kiker, M. Milfelner, Adaptive controller design for feedrate maximization of machining process, Journal of Achievements in Materials anc Manufacturing Engineering 17 (2006) 237-240.
- [4] J. Balic, A new NC machine tool controller for step-by-step milling, International Journal of Advanced Manufacturing Technology 18 (2001) 399-403.
- [5] J. Balic, Optimization of cutting process by GA approach, Robotics and Computer Integrated Manufacturing 19 (2003) 113-121.
- [6] Y. Shi, R. Eberhart, Parameter selection in particle swarm optimization, In Evolutionary Programming VII: Proc. EP98, New York: Springer-Verlag, 1998, 591-600.
- [7] Y.Y. Peng, A Discrete Particle Swarm algorithm for optimal polygonal approximation of digital curves, Journal of Visual Communication and Image Representation 15 (2004) 241-260.
- [8] E. Ozcan, C. Mohan, Analysis of a simple Particle Swarm Optimization system, Intelligent Engineering Systems Through Artificial Neural Networks 8 (1998) 253-258.
- [9] M.A. Abido, Optimal Power Flow Using Particle Swarm Optimization, International Journal of Electrical Power & Energy Systems 24 (2002) 563-571.
- [10] W. Grzesik, J. Rech, T. Wanat, Surface integrity of hardened steel parts in hybrid machining operations, Journal of Achievements in Materials and Manufacturing Engineering 18 (2006) 367-370.
- [11] C. Chen, M. Zhibin, An intelligent approach to non-constant feed rate determination for high-performance 2D CNC milling, International Journal of Manufacturing Technology and Management 9 (2006) 219-236.
- [12] S. Zhang, A. Xing, L. Jianfeng and F. Xiuli, Failure analysis on clamping bolt of milling cutter for high-speed machining, International Journal of Machining and Machinability of Materials 1 (2006) 343-353.
- [13] M. Sokovic, M. Cedilnik, J. Kopac, Use of 3D-scanning and reverse engineering by manufacturing of complex shapes, Proceedings of the 13th International Scientific Conference Achievements in Mechanical and Materials Engineering, AMME'2005, Gliwice-Zakopane, 2005, 601-604.
- [14] L.A. Dobrzański, K. Golombek, J. Kopac, M. Sokovic, Effect of depositing the hard surface coatings on properties of the selected cemented carbides and tool cermets, Journal of Materials Processing Technology 157-158 (2004) 304-311.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BOS3-0017-0043