Image processing for chatter identification in machining processes
Identifying chatter or intensive self-excited relative tool–workpiece vibration is one of the main challenges in the realization of automatic machining processes. Chatter is undesirable because it causes poor surface finish and machining accuracy, as well as reducing tool life. The identificati...
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| Online Access: | http://irep.iium.edu.my/23629/ http://irep.iium.edu.my/23629/1/image_processing_in_machining.pdf |
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iium-236292012-04-24T06:38:16Z http://irep.iium.edu.my/23629/ Image processing for chatter identification in machining processes Khalifa, Othman Omran Densibali , Amirasyid Faris, Waleed Fekry TJ227 Machine design and drawing Identifying chatter or intensive self-excited relative tool–workpiece vibration is one of the main challenges in the realization of automatic machining processes. Chatter is undesirable because it causes poor surface finish and machining accuracy, as well as reducing tool life. The identification of chatter is performed by evaluating the surface roughness of a turned workpiece undergoing chatter and chatter-free processes. In this paper, an image-processing approach for the identification of chatter vibration in a turning process was investigated. Chatter is identified by first establishing the correlation between the surface roughness and the level of vibration or chatter in the turning process. Images from chatter-free and chatter-rich turning processes are analyzed. Several quantification parameters are utilized to differentiate between chatter and chatter-free processes. The arithmetic average of gray level Ga is computed. Intensity histograms are constructed and then the variance, mean, and optical roughness parameter of the intensity distributions are calculated. The surface texture analysis is carried out on the images using a second-order histogram or co-occurrence matrix of the images. Analysis is performed to investigate the ability of each technique to differentiate between a chatter-rich and a chatter-free process. Finally, a machine vision system is proposed to identify the presence of chatter vibration in a turning process. Springer 2006-02-15 Article PeerReviewed application/pdf en http://irep.iium.edu.my/23629/1/image_processing_in_machining.pdf Khalifa, Othman Omran and Densibali , Amirasyid and Faris, Waleed Fekry (2006) Image processing for chatter identification in machining processes. International Journal of Advanced Manufacturing Technology, 31. pp. 443-449. ISSN 1433-3015 (O); 0268-3768 (P) |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
TJ227 Machine design and drawing |
| spellingShingle |
TJ227 Machine design and drawing Khalifa, Othman Omran Densibali , Amirasyid Faris, Waleed Fekry Image processing for chatter identification in machining processes |
| description |
Identifying chatter or intensive self-excited relative
tool–workpiece vibration is one of the main challenges
in the realization of automatic machining processes. Chatter
is undesirable because it causes poor surface finish and
machining accuracy, as well as reducing tool life. The
identification of chatter is performed by evaluating the
surface roughness of a turned workpiece undergoing chatter
and chatter-free processes. In this paper, an image-processing
approach for the identification of chatter vibration in
a turning process was investigated. Chatter is identified by
first establishing the correlation between the surface
roughness and the level of vibration or chatter in the
turning process. Images from chatter-free and chatter-rich
turning processes are analyzed. Several quantification
parameters are utilized to differentiate between chatter
and chatter-free processes. The arithmetic average of gray
level Ga is computed. Intensity histograms are constructed
and then the variance, mean, and optical roughness
parameter of the intensity distributions are calculated.
The surface texture analysis is carried out on the images
using a second-order histogram or co-occurrence matrix of
the images. Analysis is performed to investigate the ability
of each technique to differentiate between a chatter-rich
and a chatter-free process. Finally, a machine vision system
is proposed to identify the presence of chatter vibration in a
turning process. |
| format |
Article |
| author |
Khalifa, Othman Omran Densibali , Amirasyid Faris, Waleed Fekry |
| author_facet |
Khalifa, Othman Omran Densibali , Amirasyid Faris, Waleed Fekry |
| author_sort |
Khalifa, Othman Omran |
| title |
Image processing for chatter identification in machining processes |
| title_short |
Image processing for chatter identification in machining processes |
| title_full |
Image processing for chatter identification in machining processes |
| title_fullStr |
Image processing for chatter identification in machining processes |
| title_full_unstemmed |
Image processing for chatter identification in machining processes |
| title_sort |
image processing for chatter identification in machining processes |
| publisher |
Springer |
| publishDate |
2006 |
| url |
http://irep.iium.edu.my/23629/ http://irep.iium.edu.my/23629/1/image_processing_in_machining.pdf |
| first_indexed |
2023-09-18T20:35:43Z |
| last_indexed |
2023-09-18T20:35:43Z |
| _version_ |
1777409031558660096 |