In-Process Detection of Cutting Tool Damage by Acoustic Emission Measurement

Inasaki, Ichiro; Yonetsu, Sakae

doi:10.1007/978-1-349-06281-2_33

Cited by 27 publications

(8 citation statements)

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“…A similar result was found by [18] for turning of carbon steel (S45C according to JIS) with a P20 carbide tool (Fig. 7).…”

Section: Fig 4 Mean Ae Rms Voltage Vs (A) Width Of Cut and (B) Feedsupporting

confidence: 83%

“…The same was observed in [18] for turning of carbon steel with carbide tools; in this case the AE parameter used was the mean AE amplitude level.…”

Section: Discussionmentioning

confidence: 64%

“…It is interesting to observe that, as already mentioned, in [12], [17] and [18], where the RMS voltage or the AE mean amplitude level were used as AE parameters for the monitoring of turning of mild steel with HSS tools or carbon steel with a carbide tool, respectively, AE activity remains essentially constant (Figs. 6 and 7) as feed rate and depth of cut increase.…”

Section: Effect Of Feed Speed and Depth Of Cutmentioning

confidence: 95%

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Modeling and Experimental Analysis of Acoustic Emission from Metal Cutting

Teti

Dornfeld

1989

Journal of Engineering for Industry

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Testing parameters characterizing acoustic emission (AE) detected during metal cutting may be theoretically correlated, in a simple manner, to work material properites, cutting conditions, and tool geometry. Experimental results, obtained during turning by different researchers using different AE techniques, are presented and critically assessed with reference to their reciprocal agreement as well as their agreement with theoretical predictions. A review of current methods for AE analysis is also presented and the correlations between different AE parameters and energy and jkiwer of the detected signals are reported.

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“…A similar result was found by [18] for turning of carbon steel (S45C according to JIS) with a P20 carbide tool (Fig. 7).…”

Section: Fig 4 Mean Ae Rms Voltage Vs (A) Width Of Cut and (B) Feedsupporting

confidence: 83%

“…The same was observed in [18] for turning of carbon steel with carbide tools; in this case the AE parameter used was the mean AE amplitude level.…”

Section: Discussionmentioning

confidence: 64%

Section: Effect Of Feed Speed and Depth Of Cutmentioning

confidence: 95%

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Modeling and Experimental Analysis of Acoustic Emission from Metal Cutting

Teti

Dornfeld

1989

Journal of Engineering for Industry

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“…It also indicates that the first principal component is describing a particular characteristic of the system which is present irrespective of the condition of the cutting tool or the cutting conditions of the process. Figures 9-11 give three plots of 'Ye vs. j (one from each data set, 1, 2, and 3), for j=l ,2,...,46,where CX! = (a,,,CX,~,.…”

Section: And E Kannatey-asibu Jrmentioning

confidence: 99%

Statistical process control of acoustic emission for cutting tool monitoring

Houshmand

Kannatey‐Asibu

1989

Mechanical Systems and Signal Processing

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The problem of cutting process monitoring has been investigated in recent years, with encouraging results, using pattern recognition analysis of acoustic emission (AE) signals. The analyses are based on linear discriminant functions, which assume that the observed data (from each class) are independent random samples from multivariate normal distributions with equal covariance matrices. However, in a number of practical situations some (or all) of these assumptions may not necessarily hold, resulting in errors in the analysis. In this paper, the distributions of AE spectra generated in earlier work are first analysed, and the results indicate departure from the assumptions, although the lack of normality was not too severe. Relaxing the assumption of equality of the covariance matrices, quadratic discriminant function analysis produced improved results for tool wear and chip noise monitoring while degrading tool fracture detection. The latter is due to inadequacy of the amount of data used in training the system. It is expected that increasing the data base would improve the results for all classes. The analysis until now has focused on reducing the dimensionality of the feature space by eliminating the features with the least discriminatory power. Even though this inevitably reduces the performance of the system, it is a necessary compromise for increased computational speed. To make use of the entire feature set with a reduced matrix rank, a principal component analysis is investigated. The result is a substantial improvement in correct classification of AE signals, even under different cuting conditions.

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“…A more sophisticated way of using the power spectrum is to compare the total power in various sub-bands [3], [6]. These simple approaches give surprisingly good results in many cases.…”

Section: Introductionmentioning

confidence: 99%