Grinding Vibration Detection Using a Neural Network

Author:

Chen X1,Rowe W B1,Li Y1,Mills B1

Affiliation:

1. AMT Research Laboratory, Liverpool John Moores University, Liverpool

Abstract

The amplitude of grinding vibration increases gradually throughout the grinding wheel wear process. In the meantime the predominant vibration frequency shifts in a region close to a natural frequency of the system. The complex time-varying pattern of vibrations makes it a problem to objectively identify when the grinding vibration becomes unacceptable and when the wheel should be redressed. A neural network approach method was proposed in this paper to identify the wheel life. The signal data were pre-treated by eight-band-pass filters, which covered the whole frequency range of the grinding chatter. These pre-treated data were used as the inputs to the neural network. By training the neural network, an objective criterion can be determined for the wheel redress life.

Publisher

SAGE Publications

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering

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