One of the stages of quality control in porcelain producing factories is sorting that do with human eyes. Machine vision , including new methods for defect detection and sorting of different products. In this study, with defects diagnosis and as a result sorting porcelain, use from linear structured light pattern, triangulation techniques and rules governing mirrors. Also, among the defects occurred on porcelain, some defects that changes geometry, perimeter and texture of dishes surface, such as distortion, drops, depression, pin-hole, Concavity of floor have examined. With radiation linear structured light pattern on porcelains surface, shoot a picture from profiles reflected. Then, for processing and features extraction, transferred pictures to computers and after diagnosis kind of defect, get a criterion to determine the degree of dishes. Then, get the degree of the quality of dishes, by considering the defined numerically table ranking. Finally, by using proposed algorithms in this study, 2250 dishes with a predetermined degree have examined. The accuracy of the Concavity floor is 97.66% and the accuracy of the pin-hole is 98.5% appointed.
hosseininia, J., emam, M., & khalili, K. (2021). qualitative sorting of porcelain dishes using machine vision. Soft Computing Journal, 5(1), 66-77.
MLA
javad hosseininia; mohammad emam; khalil khalili. "qualitative sorting of porcelain dishes using machine vision". Soft Computing Journal, 5, 1, 2021, 66-77.
HARVARD
hosseininia, J., emam, M., khalili, K. (2021). 'qualitative sorting of porcelain dishes using machine vision', Soft Computing Journal, 5(1), pp. 66-77.
VANCOUVER
hosseininia, J., emam, M., khalili, K. qualitative sorting of porcelain dishes using machine vision. Soft Computing Journal, 2021; 5(1): 66-77.
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