QUANTITATIVE ANALYSIS OF TOOL WEAR IN DEEP HOLE DRILLING

Abstract

This paper presents an experimental procedure for the automatic detection and quantitative evaluation of surface wear on drills used in deep hole drilling processes. The analysis was based on images of the cutting edge captured by an industrial camera under controlled conditions, with a particular focus on the curved edge segment, which is especially prone to wear. A custom image processing algorithm was developed within the MATLAB programming environment, employing a multi-stage approach—preprocessing, surface condition classification, and distance analysis of worn zones from the curved edge—to calculate VB_mean as an indicator of wear severity. The algorithm successfully distinguishes between healthy regions, grinding marks, mechanical damage, and active wear, significantly reducing the risk of misinterpretation. Quantitative analysis performed on a dataset of 50 samples demonstrated repeatability of the results and potential for further industrial application. The observed wear patterns may serve as a basis for optimizing process parameters and implementing predictive maintenance strategies. The proposed methodology represents a step toward automated tool condition monitoring under demanding machining conditions, with the potential for integration into broader technical diagnostics systems.