Measurement Of Surface Roughness Using Image Processing Technique
Measurement of surface roughness after completion of a machining process is a common procedure undertaken in order to analyse the quality of the end product so that the desired results are determined. Similar process will be repeated if the desired surface roughness quality is not achieved. Curre...
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| Format: | Thesis |
| Language: | English English |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/7091/1/Measurement_Of_Surface_Roughness_Using_Image_Processing_Technique_-_24_Pages.pdf http://eprints.utem.edu.my/id/eprint/7091/2/Measurement_Of_Surface_Roughness_Using_Image_Processing_Technique_-_Full_Taxt.pdf |
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| Summary: | Measurement of surface roughness after completion of a machining process is a
common procedure undertaken in order to analyse the quality of the end product so
that the desired results are determined. Similar process will be repeated if the desired
surface roughness quality is not achieved. Currently, contact method is applied and is
commonly used where a stylus is drag across the surface of the work piece to obtain
its profile. In this method, the work piece needs to be removed and placed on flat
surface before measured. Therefore additional time required for setup, calibration,
work piece removal and putting is back into machine holder. Time means cost in
production. In order to reduce or eliminate non value added time, an alternative
technique of measuring or determine surface roughness is required. Machining
parameters like the cutting speed and feed will be set for rough cut and finishing cut
for both material mild steel and aluminium. For mild steel rough cut speed will be
27000 rpm and feed will be 0.2 mm, while for finishing cut speed will be 30000 rpm
and feed is 0.05mm. For aluminium rough cut speed is 62500 rpm with feed 0.5 and
finishing cut speed 95000 rpm with feed 0.15mm. This research started with the
application of vision camera in capturing work piece's surface images and using
image processing technique through MATLAB computing software to calculate the
Grey Level Co-occurrence Matrices (GLCM) features such as contrast, entropy,
energy, homogeneity and correlations. Collected data will. then be analysing using
data mining to extract the data patterns for both material aluminium and mild steel
surface. For aluminium, only 4 GLCM features are computed contributing to
influence surface roughness that is homogeneity, energy, contrast and entropy.
Whereas mild steel all 5 features were computed affect the roughness value. With the
aid of latest software, information can be easily extracted and be analyse for research
purposes and also be used in manufacturing, therefore this research will lead into
development of automated intelligent machine tools. |
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