Detection Of Chipping In Ceramic Cutting Inserts From Workpiece 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-pro...
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| Format: | Thesis |
| Language: | English |
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2017
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| Online Access: | http://eprints.usm.my/36797/ http://eprints.usm.my/36797/1/LEE_WOON_KIOW_24_Pages.pdf |
| _version_ | 1848878013509271552 |
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| author | Kiow, Lee Woon |
| author_facet | Kiow, Lee Woon |
| author_sort | Kiow, Lee Woon |
| building | USM Institutional Repository |
| collection | Online Access |
| description | 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 toolchipping 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 xxiv sensitive to chipping and thus can be used as an indicator to detect the occurrence of the tool chipping in ceramic inserts. |
| first_indexed | 2025-11-15T17:24:35Z |
| format | Thesis |
| id | usm-36797 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T17:24:35Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-367972019-04-12T05:25:06Z http://eprints.usm.my/36797/ Detection Of Chipping In Ceramic Cutting Inserts From Workpiece Profile Signature During Turning Process Using Machine Vision Kiow, Lee Woon TJ1-1570 Mechanical engineering and machinery 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 toolchipping 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 xxiv sensitive to chipping and thus can be used as an indicator to detect the occurrence of the tool chipping in ceramic inserts. 2017 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/36797/1/LEE_WOON_KIOW_24_Pages.pdf Kiow, Lee Woon (2017) Detection Of Chipping In Ceramic Cutting Inserts From Workpiece Profile Signature During Turning Process Using Machine Vision. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | TJ1-1570 Mechanical engineering and machinery Kiow, Lee Woon Detection Of Chipping In Ceramic Cutting Inserts From Workpiece Profile Signature During Turning Process Using Machine Vision |
| title | Detection Of Chipping In Ceramic
Cutting Inserts From Workpiece
Profile Signature During Turning
Process Using Machine Vision
|
| title_full | Detection Of Chipping In Ceramic
Cutting Inserts From Workpiece
Profile Signature During Turning
Process Using Machine Vision
|
| title_fullStr | Detection Of Chipping In Ceramic
Cutting Inserts From Workpiece
Profile Signature During Turning
Process Using Machine Vision
|
| title_full_unstemmed | Detection Of Chipping In Ceramic
Cutting Inserts From Workpiece
Profile Signature During Turning
Process Using Machine Vision
|
| title_short | Detection Of Chipping In Ceramic
Cutting Inserts From Workpiece
Profile Signature During Turning
Process Using Machine Vision
|
| title_sort | detection of chipping in ceramic
cutting inserts from workpiece
profile signature during turning
process using machine vision |
| topic | TJ1-1570 Mechanical engineering and machinery |
| url | http://eprints.usm.my/36797/ http://eprints.usm.my/36797/1/LEE_WOON_KIOW_24_Pages.pdf |