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Tool Cutting Force Modeling in High Speed Milling Using PSO-BP Neural Network

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Abstract:

The theory and the algorithm of Particle swarm optimization (PSO) based for neural network training were applied in the research of the modeling of milling force in high speed machining. The PSO was used to train connection weights of multi-layer feed forward neural network until the fitness error tended to be stable. Then BP algorithm was adopted to accomplish cutting force forecasting based on optimized initial weights, which take full use of the global optimization of PSO and local accurate searching of BP. The results of simulation showed that with comparison with other BP algorithms, PSO-BP not only effectively shortens the time of training networks, but also greatly improves the accuracy of prediction and universal approximation. PSO technique can act as an alternative training algorithm for ANNs.

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Advances in Machining & Manufacturing Technology IX View Preview

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Periodical:

Key Engineering Materials (Volumes 375-376)

Pages:

515-519

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.375-376.515

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Online since:

March 2008

Authors:

Jin Xing Zheng, Ming Jun Zhang, Qing Xin Meng

Keywords:

Artificial Neural Network (ANN), Cutting Force Modeling, High Speed Machining (HSM), Particle Swarm Optimization Algorithm (PSO)

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© 2008 Trans Tech Publications Ltd. All Rights Reserved

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