It is well known that measuring surface roughness is vital to quality control of the machined work piece. Recently, vision systems have been applied in industries for quality control and online inspection. Thus, measuring surface roughness using computer vision became easier and more flexible. Texture features are one of the most important techniques that have been utilized in industries in many applications. In this paper, the texture features of the gray level co-occurrence matrix (GLCM) have been utilized to predict surface roughness of specimens machined by tuning operations. The relationship between GLCM texture features and surface roughness has been investigated to discover which texture features can be used to predict surface roughness. The correlation coefficient between each texture feature and the arithinetic average height (Ra) was calculated and discussed. The investigation showed that six texture features are highly correlated with Ra. Therefore, a software has been developed to predict surface roughness for specimens machined by turning operations using these texture features. The results showed that the maximum percentage of error between the actual Ra and the predicted Ra was about ±7%.
Gadelmawla, E. (2020). Prediction of Surface Roughness of Turning Operations Using Computer Vision.. MEJ- Mansoura Engineering Journal, 35(3), 1-13. doi: 10.21608/bfemu.2020.125054
MLA
Elamir Gadelmawla. "Prediction of Surface Roughness of Turning Operations Using Computer Vision.". MEJ- Mansoura Engineering Journal, 35, 3, 2020, 1-13. doi: 10.21608/bfemu.2020.125054
HARVARD
Gadelmawla, E. (2020). 'Prediction of Surface Roughness of Turning Operations Using Computer Vision.', MEJ- Mansoura Engineering Journal, 35(3), pp. 1-13. doi: 10.21608/bfemu.2020.125054
VANCOUVER
Gadelmawla, E. Prediction of Surface Roughness of Turning Operations Using Computer Vision.. MEJ- Mansoura Engineering Journal, 2020; 35(3): 1-13. doi: 10.21608/bfemu.2020.125054
)
View on SCiNiTO