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铁道科学与工程学报

JOURNAL OF RAILWAY SCIENCE AND ENGINEERING

Vol. 19    Special 1    September 2009

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Cutting force signal pattern recognition using hybrid neural network in end milling
Song-Tae SEONG1, Ko-Tae JO2, Young-Moon LEE3

1. Research and Development Department, Gyeongbuk Hybrid Technology Institute, Gyeongsangbuk-do, 730-701, Korea;
2. School of Mechanical Engineering, Yeongnam University, Gyeongsangbuk-do, 712-160, Korea;
3. School of Eechanical Engineering, Kyungpook National University, Daegu, 220-60, Korea

Abstract:Under certain cutting conditions in end milling, the signs of cutting forces change from positive to negative during a revolution of the tool. The change of force direction causes the cutting dynamics to be unstable which results in chatter vibration. Therefore, cutting force signal monitoring and classification are needed to determine the optimal cutting conditions and to improve the efficiency of cut. Artificial neural networks are powerful tools for solving highly complex and nonlinear problems. It can be divided into supervised and unsupervised learning machines based on the availability of a teacher. Hybrid neural network was introduced with both of functions of multilayer perceptron (MLP) trained with the back-propagation algorithm for monitoring and detecting abnormal state, and self organizing feature map (SOFM) for treating huge datum such as image processing and pattern recognition, for predicting and classifying cutting force signal patterns simultaneously. The validity of the results is verified with cutting experiments and simulation tests.

 

Key words: end milling; cutting force signals; multilayer perceptrons (MLP); self organizing feature map (SOFM)

ISSN 1672-7029
CN 43-1423/U

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