Optimization of the characteristic parameters in milling using the PSO evolution technique;Optimiranje značilnih parametrov frezanja z uporabo razvojne tehnike optimizacije jate delcev

Strojniski Vestnik/Journal of Mechanical Engineering

Volumn 53, Issue 6, 2007, Pages 354-368

  Župerl, Uroš ^a   Čuš, Franci ^a   Gecevska, Valentina ^b,c,d  

^a UNIVERSITY OF MARIBOR   (Slovenia)

^b Fakulteta za Strojni tvo v Skopju

^c Univerza v Skopju   (Macedonia)

^d SS CYRIL AND METHODIUS UNIVERSITY   (Macedonia)

Author keywords

Cutting; Cutting parameters; End milling; Neural networks

Indexed keywords

MACHINING PARAMETERS; PARTICLE SWARM OPTIMIZATION (PSO); PREDICTIVE MODELS;

GENETIC ALGORITHMS; MACHINING; MATHEMATICAL MODELS; NEURAL NETWORKS; PARAMETER ESTIMATION; SIMULATED ANNEALING;

PROCESS PLANNING;

EID: 34548759609     PISSN: 00392480     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (17)

1. Župerl, U., F. Čuš (2004) Določevanje značilnih tehnoloških in gospodarskih parametrov med postopkom odrezovanja = A determination of the characteristic technological and economic parameters during metal cutting. Stroj, vestn., 5(2004), pp. 252-266.
2. Čuš, F., J. Balič (2003) Optimization of cutting process by GA approach. Robot, Comput. Integr. Manuf 19(2003), pp. 113-121.
3. Župerl, U., F. Čuš (2003), Optimization of cutting conditions during cutting by using neural networks, Robot. Comput. Integr. Manuf., 19(2003),pp. 189-199.
4. Balič, J. (2000) A new NC machine tool controller for step-by-step milling, Int. J. Adv. Manuf. Technol. 8(2000) pp. 399-403.
5. Ozcan, E., C. Mohan (1998), Analysis of a simple Particle Swarm Optimization system, Intelligent Engineering Systems Through Artificial Neural Networks, 8(1998), pp. 253-258.
6. Shi Y., R. Eberhart (1998), Parameter selection in Particle Swarm Optimization, In Evolutionary Programming VII: Proc. EP98, New York: Springer-Verlag (1998), pp. 591-600.
7. Angeline, P.J. Evolutionary optimization versus Particle Swarm Optimization: philosophy and performance differences, in: Proceedings of the 7th ICEC, (1998), pp. 601-610.
8. Kennedy, J., R.C. Eberhart (1995) Particle Swarm Optimization, Proceedings of IEEE Int. Conference on Neural Networks, 4(1995), pp. 1942-1948.
9. Reynolds, C. (1990) Flocks, herds, and schools: a distributed behavioural model, Computer Graphics, 21(1990),pp. 25-34.
10. R.C., Eberhart, Y. Shi (2003), Comparison between genetic algorithm and Particle Swarm Optimization, Proceedings of the 7th ICEC, (1998), pp. 611-616.
11. Boyd, J. (2003), Thinking is Social: Experiments with the adaptive culture model, Journal of Conflict Resolution, 42(1998), pp. 56-76.
12. Balic J. (2003), Optimization of cutting process by GA approach, Robot. Comput. Integr. Manuf. 19(2003) 113-121.
13. He, Z., C. Wei, L. Yang, X. Gao, S. Yao, R. Eberhart, Y.: Shi, (1998), Extracting rules from fuzzy neural network by Particle Swarm Optimization, Proc. of IEEE International Conference on Evolutionary Computation (ICEC'98), (1998), pp. 66-71.
14. Cus, F., U. Zuperl, E. Kiker, M. Milfelner (2006), Adaptive controller design for feedrate maximization of machining process, Journal of Achievements in Materials and Manufacturing Engineering 17(2006),pp. 237-240.
15. Abido M.A. (2002), Optimal power flow using Particle Swarm Optimization, International Journal of Electrical Power & Energy Systems, 24(2002), pp. 563-571.
16. Kopač, J. (2002) Rezalne sile in njihov vpliv na gospodarnost obdelave = Cutting forces and their influence on the economics of machining. Stroj, vestn. 3 (2002), pp. 121-132.
17. Župerl, U., F. Čuš (2004) Tool cutting force modeling in ball-end milling using multilevel perceptron, J. Mater. Process. Technol., (2004), pp. 67-72.