Effects of Tool Wear on the Surface Texture in Turning: A Feature Characterization Approach Based on ISO 21920-2

Abstract

The surface texture of a turned component acts as a fingerprint of both the process parameters and the tool wear condition, imaging the cutting edge. Classical surface parameters such as $R_\mathrm{a}$ or $R_\mathrm{q}$ describe the topography only globally and allow no spatially resolved evaluation of the process-induced deterministic structures. This work investigates how far the feature characterization standardized in ISO 21920-2 makes this wear information accessible and physically interpretable. The database comprises roughness profiles of twelve AlTiN-coated carbide inserts (CNMG120408) machining normalized AISI 1045 steel, measured at nine wear states over the entire tool life, with three replicate profiles per state. The correlation of standardized field and feature parameters with crater wear, flank wear, and cutting time is first examined. Watershed segmentation is then adapted to extract the rotational tool grooves and evaluate their geometry statistically. A newly developed mean-feature approach decomposes the profile into a deterministic and a stochastic component. Wear-induced changes are almost entirely carried by the deterministic component, and within it by the trailing flank of the cutting groove. A comparison with confocal measurements confirms that the mean feature reconstructs the engaged cutting edge geometry, with the trailing-flank steepening attributable to notch wear on the secondary cutting edge. An exhaustive evaluation of more than 920,000 feature characterization combinations and multivariate models reveals that the groove-level mean maximum absolute gradient $\overline{R_\mathrm{dt}}_\mathrm{groove}$ alone explains 83-90% of the variance of the wear indicators, so that a single, physically motivated parameter suffices for robust wear estimation. A follow-up study will investigate inline monitoring using scattered light sensors.