Detection of chipping in ceramic cutting inserts from workplace profile signature during turning process using machine vision
Ceramic tools are prone to chipping due to their low impact toughness. Tool chipping significantly decreases the surface finish quality and dimensional accuracy of the workpiece. Thus, in-process detection of chipping in ceramic tools is important especially in unattended machining. Existing in-proc...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
2017
|
| Subjects: | |
| Online Access: | http://eprints.usm.my/36797/1/LEE_WOON_KIOW_24_Pages.pdf http://eprints.usm.my/36797/1/LEE_WOON_KIOW_24_Pages.pdf http://eprints.uthm.edu.my/10262/1/Lee_Woon_Kiow.pdf |
| Summary: | Ceramic tools are prone to chipping due to their low impact toughness. Tool
chipping significantly decreases the surface finish quality and dimensional accuracy
of the workpiece. Thus, in-process detection of chipping in ceramic tools is
important especially in unattended machining. Existing in-process tool failure
detection methods using sensor signals have limitations in detecting tool chipping.
The monitoring of tool wear from the workpiece profile using machine vision has
great potential to be applied in-process, however no attempt has been made to detect
tool chipping. In this work, a vision-based approach has been developed to detect
tool chipping in ceramic insert from 2-D workpiece profile signature. The profile of
the workpiece surface was captured using a DSLR camera. The surface profile was
extracted to sub-pixel accuracy using invariant moment method. The effect of
chipping in the ceramic cutting tools on the workpiece profile was investigated using
autocorrelation function (ACF) and fast Fourier transform (FFT). Detection of onset
tool chipping was conducted by using the sub-window FFT and continuous wavelet
transform (CWT). Chipping in the ceramic tool was found to cause the peaks of ACF
of the workpiece profile to decrease rapidly as the lag distance increased and
deviated significantly from one another at different workpiece rotation angles. From
FFT analysis the amplitude of the fundamental feed frequency increases steadily with
cutting duration during gradual wear, however, fluctuates significantly after tool has
chipped. The stochastic behaviour of the cutting process after tool chipping leads to a
sharp increase in the amplitude of spatial frequencies below the fundamental feed
frequency. CWT method was found more effective to detect the onset of tool
chipping at 16.5 s instead of 17.13 s by sub-window FFT. Root mean square of CWT
coefficients for the workpiece profile at higher scale band was found to be more sensitive to chipping and thus can be used as an indicator to detect the occurrence of
the tool chipping in ceramic inserts. |
|---|