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

Abstract:

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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.

Info:

Periodical:

Advanced Materials Research (Volumes 335-336)

Edited by:

Yun-Hae Kim, Prasad Yarlagadda, Xiaodong Zhang and Zhijiu Ai

Pages:

1473-1476

DOI:

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

Citation:

B. D. Li "Cutting Parameters Optimization Based on Radial Basis Function Neural Network and Particle Swarm Optimization", Advanced Materials Research, Vols. 335-336, pp. 1473-1476, 2011

Online since:

September 2011

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Price:

$35.00

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