Correlation study of tool flank wear with machined surface texture in end milling

• Study of the cutting tool condition from end-milled surface images.
• End milling of steel workpiece with coated carbide tool is done.
• Compensation of inhomogeneous illumination of surface images has been done.
• GLCM and RLS analysis of processed images are done for progressive tool wear.
• Feature selection is done based on the correlation with tool wear.

Indirect tool condition monitoring technique using surface texture analysis is gaining a parallel improvement with the advances of digital image processing techniques with the advent of high- end machine vision systems for fulfilment of high product quality. In this work, condition monitoring of HSS mills and coated carbide milling inserts has been performed by analyzing the resulting end-milled surface images using image texture analyses. The machined surface images were pre-processed by recovering them from inhomogeneous illumination and then two texture analysis methods, namely, gray level co-occurrence matrix (GLCM) and run length statistical (RLS) techniques were applied on the pre-processed images. Texture descriptors obtained have been highly correlated with the trend of flank wear. Finally a selection of texture features, namely, contrast and GLN, has been made within those extracted texture features for best correlation with tool wear values.