Characterization of wear and prediction of wear zone locations on the rake face using Mamdani fuzzy inference system

Abstract

In order to improve the performance of the cutting tool, third-generation tools with multi-layered nanocoatings on the rake face are used. During machining, the chip–tool interactions depict that although the tool wear on the rake face is located in the close proximity of the cutting edge, that is, within 800 µm, all the commercially available cutting tools have the coatings on the entire rake face. Taking into account the tribological properties required by the rake face close to the cutting edge, that is, high wear resistance and low friction, this study makes an attempt to identify, characterize and locate the actual wear zones/regions in terms of hard and soft zones in the chip contact area of tungsten carbide (WC) inserts close to the cutting edge in turning. Mamdani fuzzy inference system model was developed, trained with the sample experimental data and tested with the test data. The simulated results showed that the average error values of edge chipping (in X- and Y-directions), nose damage and crater wear (in X- and Y-directions) are about 2.37%, 3.01%, 2.86%, 2.66% and 1.89%, respectively. The fuzzy model developed in this study showed remarkable prediction of the wear zone locations and is also helpful for the researchers to decide the type of coating (hard and soft) along the specified zones for reducing the cost of production.