Machine vision application for machined components surface roughness assessment in the micro and nano-scale regions
The commonly used method for surface roughness measurement in industrial applications is the direct method by using a measuring stylus [Lo et al. 2005]. Stylus techniques have great inherent limitations, such as the fragility of the instrument, the possible surface scratching, and the limited accura...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
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Curran Associates
2006
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| Online Access: | http://hdl.handle.net/20.500.11937/11820 |
| _version_ | 1848747907873767424 |
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| author | Al-Kindi, G. Shirinzadeh, B. Zhong, Yongmin |
| author2 | Billingsley, John, ed. |
| author_facet | Billingsley, John, ed. Al-Kindi, G. Shirinzadeh, B. Zhong, Yongmin |
| author_sort | Al-Kindi, G. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The commonly used method for surface roughness measurement in industrial applications is the direct method by using a measuring stylus [Lo et al. 2005]. Stylus techniques have great inherent limitations, such as the fragility of the instrument, the possible surface scratching, and the limited accuracy due to probe tip radius. In addition, only 2D surface topography is acquired with stylus techniques [Jetley et al. 1993].The development of non-contact based roughness measurement techniques for engineering surfaces has received considerable attention. The non-contact based roughness measurement techniques aim to find alternative ways to permit rapid surface roughness measurement with acceptable accuracy. One of the most promising non-contact based roughness measurement techniques is the computer vision technique [Li et al. 2004]. However, practical surface roughness measurement based on computer vision technology is still difficult [Lee and Tarng, 2001]. |
| first_indexed | 2025-11-14T06:56:37Z |
| format | Conference Paper |
| id | curtin-20.500.11937-11820 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:56:37Z |
| publishDate | 2006 |
| publisher | Curran Associates |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-118202017-02-28T01:33:31Z Machine vision application for machined components surface roughness assessment in the micro and nano-scale regions Al-Kindi, G. Shirinzadeh, B. Zhong, Yongmin Billingsley, John, ed. The commonly used method for surface roughness measurement in industrial applications is the direct method by using a measuring stylus [Lo et al. 2005]. Stylus techniques have great inherent limitations, such as the fragility of the instrument, the possible surface scratching, and the limited accuracy due to probe tip radius. In addition, only 2D surface topography is acquired with stylus techniques [Jetley et al. 1993].The development of non-contact based roughness measurement techniques for engineering surfaces has received considerable attention. The non-contact based roughness measurement techniques aim to find alternative ways to permit rapid surface roughness measurement with acceptable accuracy. One of the most promising non-contact based roughness measurement techniques is the computer vision technique [Li et al. 2004]. However, practical surface roughness measurement based on computer vision technology is still difficult [Lee and Tarng, 2001]. 2006 Conference Paper http://hdl.handle.net/20.500.11937/11820 Curran Associates restricted |
| spellingShingle | Al-Kindi, G. Shirinzadeh, B. Zhong, Yongmin Machine vision application for machined components surface roughness assessment in the micro and nano-scale regions |
| title | Machine vision application for machined components surface roughness assessment in the micro and nano-scale regions |
| title_full | Machine vision application for machined components surface roughness assessment in the micro and nano-scale regions |
| title_fullStr | Machine vision application for machined components surface roughness assessment in the micro and nano-scale regions |
| title_full_unstemmed | Machine vision application for machined components surface roughness assessment in the micro and nano-scale regions |
| title_short | Machine vision application for machined components surface roughness assessment in the micro and nano-scale regions |
| title_sort | machine vision application for machined components surface roughness assessment in the micro and nano-scale regions |
| url | http://hdl.handle.net/20.500.11937/11820 |