Tool Wear Classification Using Decision Treesin 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 dif...

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Bibliographic Details
Published in:Procedia engineering Vol. 69; pp. 1326 - 1335
Main Authors: Klaic, Miho, Staroveski, Tomislav, Udiljak, Toma
Format: Journal Article
Language:English
Published: Elsevier Ltd 2014
Subjects:
machine learning
stone drilling
tool condition monitoring
tool wear
tool wear
stone drilling
machine learning
tool condition monitoring
ISSN:1877-7058, 1877-7058
Online Access:Get full text
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Summary: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.
ISSN:1877-7058
1877-7058
DOI:10.1016/j.proeng.2014.03.125