A Hybrid Approach of ANN and HMM for Cutting Chatter Monitoring

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The Hidden Markov Model (HMM) offers a powerful framework for temporal modeling of features extracted from time varying signals, and the Artificial Neural Network (ANN) has been widely used for pattern recognition, time series prediction, and optimization and forecasting. This paper describes a hybrid HMM/ANN approach which is a very competitive alternative to standard HMM for cutting chatter monitoring both in terms of performances and recognition accuracy. The hybrid HMM/ANN system uses ANN, usually a Multi-layer Perceptron ANN, to integrate the multi-stream inputs as feature transformation, whose goal is to take the advantages from the properties of both HMM and ANN. Experimental results show the efficiency of the hybrid system in monitoring of cutting process.

Info:

Periodical:

Advanced Materials Research (Volumes 97-101)

Edited by:

Zhengyi Jiang and Chunliang Zhang

Pages:

3225-3232

DOI:

10.4028/www.scientific.net/AMR.97-101.3225

Citation:

C. L. Zhang et al., "A Hybrid Approach of ANN and HMM for Cutting Chatter Monitoring", Advanced Materials Research, Vols. 97-101, pp. 3225-3232, 2010

Online since:

March 2010

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$38.00

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