Zheng, Kougen (1992) Application of the Wigner distribution to monitoring cutting tool condition. PhD thesis, University of Warwick.

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Abstract

This thesis is about the application of the Wigner distribution to cutting tool
monitoring and control. After reviewing traditional methods, a new method is
proposed. This is to regard the surface texture and geometric error of form of
a machined workpiece as the fingerprint of a cutting process, to analyse it, and
to extract cutting tool vibration information from it, which can then be used for
cutting tool monitoring.
In order to analyse the surface texture effectively, three analysing tools, i.e.
the Fourier transform, the ambiguity function, the Wigner distribution (WD),
are examined and compared with each other, and it is concluded that the WD
is best able to analyse both stationary and nonstationary signals. Furthermore,
computer simulation of both chirp signals and frequency modulated signals is
then carried out, and it is shown that the WD can be used to extract useful
parameters successively.
In order to demonstrate the suitability of the WD for machine tool condi-
tion monitoring, first cutting tool vibration are measured directly by two linear
variable differential transformers mounted on the cutting tool, and then these
measured data about vibration are used to verify those parameters extracted
from the surface of the machined workpiece by the WD. It is found that
• the extracted frequencies in both horizontal and vertical direction are within
10% of those measured,
• the extracted amplitudes in both horizontal and vertical direction are highly
correlated with those measured.
This result confirms the feasibility of this technique. In spite of being an
off-line process, this technique is simple, reliable, and can reveal the direct effect
of cutting processes.

Item Type:	Thesis (PhD)
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Official Date:	31 May 1992
Dates:	Date Event 31 May 1992 Submitted
Institution:	University of Warwick
Theses Department:	School of Engineering
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Whitehouse, D. J. (David J.) ; Bowen, D. Keith (David Keith), 1940-
Extent:	v, 275 leaves : illustrations
Language:	eng

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