Neural Network Assisted Particle Swarm Optimization of Machining Process

Uros Zuperl; Franci Cus

doi:10.4028/www.scientific.net/KEM.581.511

Paper Titles

Achievement of Precision Accuracy of Openings in Gas Turbine Engines Details
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Stress Modelling in Curved Parts of Short Fibres Reinforced Plastic Products
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Investigation of Residual Stresses in Case of Hard Turning of Case Hardened 16MnCr5 Steel
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Validation of 3D Finite Element Simulation of Chip Removal Process Performed by Unique Insert Geometry
p.505

Neural Network Assisted Particle Swarm Optimization of Machining Process
p.511

On the Multi-Level Unit Load Formation Model
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Flexible Manufacturing System for Drawingless Manufacturing
p.527

On the Development of Machine Controller Systems with Enhanced Facility for Process Monitoring Integration
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Sensitivity Analysis of Road Transport by Sensibility of Production-Function
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HomeKey Engineering MaterialsKey Engineering Materials Vol. 581Neural Network Assisted Particle Swarm...

Neural Network Assisted Particle Swarm Optimization of Machining Process

Abstract:

In this paper, optimization system based on the artificial neural networks (ANN) and particle swarm optimization (PSO) algorithm was developed for the optimization of machining parameters for turning operation. The optimization system integrates the neural network modeling of the objective function and particle swarm optimization of turning parameters. New neural network assisted PSO algorithm is explained in detail. An objective function based on maximum profit, minimum costs and maximum cutting quality in turning operation has been used. This paper also exhibits the efficiency of the proposed optimization over the genetic algorithms (GA), ant colony optimization (ACO) and simulated annealing (SA).

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Periodical:

Key Engineering Materials (Volume 581)

Pages:

511-516

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.581.511

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Online since:

October 2013

Authors:

Uros Zuperl, Franci Cus

Keywords:

Cutting Conditions, Neural Network (NN), Optimization, Swarm Intelligence, Turning

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References

[1] F. Cus, J. Balic, Optimization of cutting process by GA approach, Robot. comput. integr. manuf. 19 (2003) 113-121.

Google Scholar

[2] F. Cus, J. Balic, Selection of cutting conditions and tool flow in flexible manufacturing system, The international journal for manufacturing science & technology 2 (2000) 101-106.

Google Scholar

[3] E. Dorigo, The ant system: Optimization by a colony of cooperating agents, IEEE Transaction on Systems, Man and Cybernetics 26 (2006) 1-13.

Google Scholar

[4] R.C. Eberhart, Y. Shi, Comparison Between Genetic Algorithm and Particle Swarm Optimization, Proceedings of the 7th ICEC, (1998) 611-616.

Google Scholar

[5] U. Zuperl, F. Cus, V. Gecevska, Optimization of the characteristic parameters in milling using the PSO evolution technique, Stroj. vestn. 53(6) (2007) 354-368.

Google Scholar

[6] J. Kopac, Cutting forces and their influence on the economics of machining, Stroj. vestn. 48(3) (2002) 121-132.

Google Scholar