Tool Wear Condition Prediction Using Vibration Signals in High Speed Machining (HSM) of Titanium (Ti-6Al-4 V) Alloy

Ti-6Al-4 V is extensively used in aerospace and bio-medical applications. In an automated machining environment monitoring of tool conditions is imperative. In this study, Experiments were conducted to classify the tool conditions during High Speed Machining of Titanium alloy. During the machining process, vibration signals were monitored continuously using accelerometer. The features from the signal are extracted and a set of prominent features are selected using Dimensionality Reduction Technique. The selected features are given as an input to the classification algorithm to decide about the condition of the tool. Feature selection has been carried out using J48 Decision Tree Algorithm. Classifications of tool conditions were carried out using Machine Learning Algorithms namely J48 Decision Tree algorithm and Artificial Neural Network (ANN). From the analysis, it is found that ANN is producing comparatively better results. The methodology adopted in this study will be useful for online tool condition monitoring.