Tool monitoring of end milling based on gap sensor and machine learning

Tool wear is a detrimental circumstance in end milling and estimating its occurrence in machinery is an onerous process. Indirect tool monitoring has been actively studied to identify instances of wear on the cutting tool based on the signal from a sensor that represents the tool condition. Runout of a machine spindle during machining as a result of a defective tool commonly occurs in the metal cutting process. In this study, gap sensors were installed at the machine spindle to measure the runout. Two types of tool conditions and four cutting depths were considered during end milling to identify the relation between the spindle runout, cutting depth, and tool condition based on the gap sensor signal. Statistical features were extracted from the signals obtained, and a feature selection technique was applied to identify the ideal features as an input for the machine learning (ML) algorithms, specifically support vector machine (SVM) and multi-layer perceptron neural network (MLP NN). The SVM models were evaluated through k-fold cross-validation, while stochastic learning was applied to the MLP NN models to obtain the most compatible algorithm for the binary classification. The performance of SVM and MLP NN algorithms in classifying the signal based on the tool condition was studied and compared. The SVM outperformed the MLP NN in terms of classification accuracy, F1-score, precision, and sensitivity for all datasets despite the minimal parameter assignment in the former.