Abstract
Quality of machined surfaces and its integrity are strongly dependent on tool wear, which consequently depends on the tool life or machining time. Therefore, prediction of the machining time of the cutting tool during machining processes is very important in order to obtain high precision parts and to reduce manual fit operations and production cost. In this work, the relationship between texture features of the gray-level co-occurrence matrix and the machining time of the cutting tool in turning operations has been investigated, and the results show that four texture features are highly correlated with machining time. These highly correlated texture features were utilized to calculate machining time of the cutting tool from captured images of machined parts. The verification results showed that the maximum percentage of error between the actual and calculated machining time ranged between -4.65% and 7.79%.