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An intelligent sensor system approach for reliable tool flank wear recognition

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Abstract

An intelligent sensor system approach for reliable flank wear monitoring in turning is described. Based on acoustic emission and force sensing, an intelligent sensor system integrates multiple sensing, advanced feature extraction and information fusion methodology. Spectral, statistical and dynamic analysis have been used to determine primary features from the sensor signals. A secondary feature refinement is further applied to the primary features in order to obtain a more correlated feature vector for the tool flank wear process. An unsupervised ART2 neural network is used for the fusion of AE and force information and decision-making of the tool flank wear state. The experimental results confirm that the developed intelligent sensor system can be reliably used to recognise the tool flank wear state over a range of cutting conditions.

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Abbreviations

\(\bar x\) :

mean

σ 2 :

variance

k n :

end condition factor of the cantilever beam

E :

Young's modulus of tool holder

I :

moment of inertia of tool holder at cross section

m :

mass of tool holder per unit length

L :

length of tool overhang

l :

the size of the moving window

fm, pm, sm, km :

the mean values of the four primary features (the tangential force component, the frequency band power, the skew, and the kurtosis)

fs, ps, ss, ks :

the standard deviation values of the four primary features

F=:

resultant feature vector

I i :

element of input vector

Y i :

output node

W i ,X i ,U i ,V i ,P i ,Q i :

parameters inF 1 layer

R i :

orienting parameter

ρ :

vigilance parameter

b ij ,t ji :

bottom-to-top and top-to-bottom weights

a, b, c :

network parameters

f():

thresholding function

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Authors and Affiliations

  1. Department of Mechanical and Production Engineering, The National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore

    Y. M. Niu,  Y. S. Wong & G. S. Hong

Authors
  1. Y. M. Niu
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  2. Y. S. Wong
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  3. G. S. Hong
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Corresponding author

Correspondence to Y. S. Wong.

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Niu, Y.M., Wong, Y.S. & Hong, G.S. An intelligent sensor system approach for reliable tool flank wear recognition. Int J Adv Manuf Technol 14, 77–84 (1998). https://doi.org/10.1007/BF01322215

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