Prognostics model for tool life prediction in milling using texture features of surface image data

In a machine tool, the cutting tool is mainly responsible for producing a component with good surface quality. With the time the cutting tool wear out and affects the surface quality. Hence it is very important to monitor the condition of the cutting tool to avoid the production of substandard parts. In this work the face milling cutter is made to interact with hardened steel components to manufacture the required surfaces with a specified amount of stock removal. The cutting conditions are selected and machining is done till the tool reaches its critical flank wear value. The captured surface images are analyzed using the statistical and spectral texture analysis methods. The flank wear of the cutting insert is measured at frequent intervals. The evaluated texture features are correlated with the flank wear using the multivariate correlation methods. The significant features are selected based on the correlation value and its mutual correlation value with other features. The selected texture features are plotted against machining time or the number of components. The developed regression model based on the selected parameters and the time is used to predict the flank wear.