Papers by Author: Qiang Wang

Abstract: A processing method based on wavelet transform for the monitoring signals of grinding wheel dull is presented. The noise-falling method based on wavelet transform is used to process AE signals, grinding force signals and the electric current signals of main axis motor produced in grinding process, and the processing results can be used to identify grinding wheel state. Test result indicates that the grinding wheel state can be identified exactly by these three kinds of signal characteristics, and have higher identified precision.

Abstract: The grinding quality assessing is a complex decision-making process, which must optimize and balance multi-influence factors. A grinding quality assessing method based on fuzzy synthetic evaluation theory combined with analytic hierarchy process (AHP) is presented in this paper, and used to quality assessing for grinding process. The result of analyzing example indicates that this method can be used to estimate grinding quality based on the monitoring result of grinding process and grinding condition, and to assist grinding worker to select optimum grinding parameters for the steady grinding quality.

Abstract: A grinding trouble on-line monitoring mode is presented based on the nonlinear building mode principle of neural network. The input units were the peak of the FFT, the peak of RMS, and the standard deviation of AE signals. The outputs were the troubles of the grinding burning, grinding chatter, and grinding wheel dull. The structure of neural network is established by self-configuration method. The network mode is trained and tested by using the experiment data, and the results indicate that the neural network mode obtained by self-configuration method has high recognize rate for grinding troubles, and can be used to monitor grinding troubles on-line.