Cutting tool wear detection using multiclass logical analysis of data

This article presents a new tool wear multiclass detection method. Based on the experimental data, tool wear classes are defined using the Douglas–Peucker algorithm.

Logical analysis of data (LAD) is then used as machine learning, pattern recognition technique for double objectives of detecting the present tool wear class based on the recent sensors’ readings of the time-dependent machining variables, and deriving new information about the intercorrelation between the tool wear and the machining variables, by doing pattern analysis.

LAD is a data-driven technique which relies on combinatorial optimization and pattern recognition.

The accuracy of LAD is compared to that of an artificial neural network (ANN) technique, since ANN is the most familiar machine learning technique.

The proposed method is applied to experimental data those are gathered under various machining conditions.

The results show that the proposed method detects the tool wear class correctly and with high accuracy.

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