engleski

Tool Wear Classification Using Decision Trees in Stone Drilling Applications: A Preliminary Study

Process parameters of stone drilling with a small diameter twist drill were used to predict tool wear by means of a machine learning decision tree algorithm. The model links tool wear with features extracted from the force sensor and the main and feed drive current sensors signals recorded under different cutting conditions and different tool wear states. Signal features extracted from both the time and frequency domain were used as input parameters for construction of a decision tree which classifies the tool state into sharp or worn. The model was refined by selecting only the feature sources most important for classification. The best model achieves 90% accuracy in classification and relies only on features of the current signals, which simplifies its implementation in a CNC system for industrial applications.

tool wear; stone drilling; tool condition monitoring; machine learning

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