Grinding Acoustic Emission Classification in Terms of Mechanical Behaviours

Xun Chen; James Griffin

doi:10.4028/www.scientific.net/KEM.329.15

Paper Titles

Implementation of Thermal Models in Grinding
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A Study on Optimum Condition of Centerless Grinding Machine for Ferrule Using Taguchi Method and Response Surface Method
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Grinding Acoustic Emission Classification in Terms of Mechanical Behaviours
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Development of Intelligent Grinding Database
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An Experimental Investigation of Optimal Grinding Condition for Aspheric Surface Lens Using Full Factorial Design
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Grinding Performance of a Grain-Arranged Diamond Wheel against Industrial Pure Aluminum
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Improvement of Machining Accuracy in Micro Cylindrical Traverse Grinding
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A Micro Ultrasonic Grinding Device with Very High Frequency and its Application
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Computer Simulations of Cylindrical Plunge Grinding - Influence of Work Stiffness on Grinding Accuracy -
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HomeKey Engineering MaterialsKey Engineering Materials Vol. 329Grinding Acoustic Emission Classification in Terms...

Grinding Acoustic Emission Classification in Terms of Mechanical Behaviours

Abstract:

The material removal in grinding involves rubbing, ploughing and cutting. For grinding process monitoring, it is important to identify the effects of these different phenomena experienced during grinding. A fundamental investigation has been made with single grit cutting tests. Acoustic Emission (AE) signals would give the information relating to the groove profile in terms of material removal and deformation. A combination of filters, Short-Time Fourier Transform (STFT), Wavelets Transform (WT), statistical windowing of the WT with the kurtosis, variance, skew, mean and time constant measurements provided the principle components for classifying the different grinding phenomena. Identification of different grinding phenomena was achieved from the principle components being trained and tested against a Neural Network (NN) representation.

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Periodical:

Key Engineering Materials (Volume 329)

Pages:

15-20

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.329.15

Citation:

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Online since:

January 2007

Authors:

Xun Chen, James Griffin

Keywords:

Acoustic Emission (AE), Grinding, Neural Network (NN), Short Time Fourier Transfer, Wavelet Transform (WT)

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