Cutting Parameters Optimization Based on Radial Basis Function Neural Network and Particle Swarm Optimization

Bao Dong Li

doi:10.4028/www.scientific.net/AMR.335-336.1473

Paper Titles

Preparation of Porous HA Bioceramics by Gelcasting and Pressureless Sintering
p.1454

Characteristics Analysis of Acoustic Emission Signals from Titanium under Tensile Fracture
p.1459

Recovery of Sc³⁺ from Red Mud Leaching Solution by Emulsion Liquid Membrane
p.1465

The Study of Using TiO₂ Nanotube Arrays as a Drug Delivery for Alendronate
p.1469

Cutting Parameters Optimization Based on Radial Basis Function Neural Network and Particle Swarm Optimization
p.1473

Preparation and Properties of Bacterial Poly (3-hydroxybutyrate-co-3-hydroxyvalerate) Fibers
p.1477

Research on Controllable Effects of HI Ultrasonic Radiation to Active Composition in Hippophae
p.1481

The Modification of VPO Catalyst Precursor and Characterization
p.1485

Biosorption Behavior of U(VI) in Wastewater Containing U(VI) by Immobilized Saccharomyces Cerevisiae
p.1489

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 335-336Cutting Parameters Optimization Based on Radial...

Cutting Parameters Optimization Based on Radial Basis Function Neural Network and Particle Swarm Optimization

Abstract:

A technique of cutting parameters optimization based on radial basis function neural networks and partical swarm optimization is presented in the paper. Taking experimental data as samples, the model between processing parameter and processing function was established based on radial basis function neural networks. Then, the cutting parameters is optimized by particle swarm optimization. With the combination of radial basis function neural network and particle swarm optimization, and making good use of the respective virtues,the model was solved.The experiment shows that the actual output as same as the predictive output and the mixes algorithm can realize optimization of cutting parameter real time in workplace.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 335-336)

Pages:

1473-1476

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.335-336.1473

Citation:

Cite this paper

Online since:

September 2011

Authors:

Bao Dong Li

Keywords:

Cutting Parameter Optimization, Particle Swarm Optimization (PSO), Radial Basis Function Neural Network (RBFNN)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Shaohua Zhu, Yanbin Huang, Qingxu Li. Machining Process[M]. Beijing: Electronic Industry Press, 2005.3(In Chinese)

Google Scholar

[2] Chun Huang, Guancheng Jiang, Chaofeng Ji, Dongliang Hu, Yanqin Shen g, Yougui Song. Research on the Formation Damage Diagnosis Based on Radial Basis Functions Neural Network [J], Journal of Basic Science and Engineering, 2010, 18(2):313-320(In Chinese)

Google Scholar

[3] Ming Chen.Neural Network model[M]. Dalian:Dalian University of Technology Press, 1995(In Chinese)

Google Scholar

[4] Wenrong Mei. Application of Neural Network in solid particles damage simulation study[J]. Oil Drilling&Production Technology, 1994, 16(4):4347(In Chinese)

Google Scholar