Two manufacturing applications of the fuzzy K-NN algorithm

doi:10.1016/S0165-0114(96)00176-5

Fuzzy Sets and Systems

Volume 92, Issue 3, 16 December 1997, Pages 289-303

https://doi.org/10.1016/S0165-0114(96)00176-5 Get rights and content

Abstract

This paper discusses the applications of the fuzzy K-NN (K-nearest neighbors) algorithm for identifying welds from digitized radiographic images and for determining PCBN (polycrystalline cubic boron nitride) tool failure in face milling operations. Both applications consist of two major steps: feature extraction and pattern classification. For the weld identification application, the weld image is processed line by line and three features are extracted for each object in the line image. These features are the width, the mean square error (MSE) between the object and its Gaussian, and the peak intensity (gray level). For the application of tool failure recognition, two features are derived from AE signals generated by the cutting operation. They are the ΔRMS and peak/count ratio. The use of the fuzzy K-NN classifier and the classification results are discussed. The results show that the fuzzy K-NN classifier yields high successful rates of recognition for both applications.

References (30)

R.X. Du et al.
Tool condition monitoring in turning using fuzzy set theory
Int. J. Machine Tools Manufact.
(1992)
A. Gayer et al.
Automatic recognition of welding defects in real-time radiography
NDT Int.
(1990)
W. Hundt et al.
An approach to monitoring of the grinding process using acoustic emission technique
Ann. CIRP
(1994)
T.J. Ko et al.
Estimation of tool wear length in finish milling using a fuzzy inference algorithm
Wear
(1993)
M. Liu et al.
Analytical modeling of acoustic emission for monitoring of peripheral milling process
Int. J. Machine Tools Manufact.
(1991)
T.S. Newman et al.
A survey of automated visual inspection
Comput. Vision Image Understanding
(1995)
R.E. Uhrig
Use of neural networks in nuclear power plants
ISA Trans.
(1993)
J. Webster et al.
Acoustic emission for process control and monitoring of surface integrity during grinding
Ann. CIRP
(1994)
L.A. Zadeh
Fuzzy sets
Inform. Control
(1965)
J. Bezdek
Pattern Recognition with Fuzzy Objective Function Algorithms
(1981)

C.H. Chen

Pattern recognition in nondestructive evaluation of materials

Handbook of Pattern Recognition and Computer Vision

(1993)

E.N. Diei et al.

Acoustic emission sensing of tool wear in face milling

J. Eng. Ind.

(1987)

S. Dmaodarasamy et al.

An inexpensive system for classifying tool wear states using pattern recognition

Wear

(1993)

D.A. Dornfeld

In process recognition of cutting states

JAME Int. J. Ser. C

(1994)

D.A. Dornfeld et al.

An investigation of grinding and wheel loading using acoustic emission

J. Eng. Ind.

(1984)

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Two manufacturing applications of the fuzzy K-NN algorithm

Abstract

Int. J. Machine Tools Manufact.

NDT Int.

Ann. CIRP

Wear

Int. J. Machine Tools Manufact.

Comput. Vision Image Understanding

ISA Trans.

Ann. CIRP

Inform. Control

Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern recognition in nondestructive evaluation of materials

Handbook of Pattern Recognition and Computer Vision

Acoustic emission sensing of tool wear in face milling

J. Eng. Ind.

An inexpensive system for classifying tool wear states using pattern recognition

Wear

In process recognition of cutting states

JAME Int. J. Ser. C

An investigation of grinding and wheel loading using acoustic emission

J. Eng. Ind.