Tool Condition Monitoring in Face Milling Process Using Decision Tree and Statistical Features of Vibration Signal

In milling process, the quality of the machined component is highly influenced by the condition of the tool. Hence, monitoring the condition of the tool becomes essential. A suitable mechanism needs to be devised in order to monitor the condition of the tool. To achieve this, condition monitoring of milling tool is taken up for the study. In this work, the condition of the tool is classified as good tool and tool with common faults in face milling process such as flank wear, worn out and breakage of the tool based on machine learning approach using statistical feature and decision tree technique. Vibration signals of the milling tool are obtained during machining of mild steel. Statistical features are extracted from the obtained signal, in which the important features are selected using decision tree. The selected features are given as the input to the same algorithm. The output of the algorithm is utilized for classifying the different conditions of the tool. The experimental results show that the accuracy of decision tree technique is at the acceptable level and can be recommended for fault diagnosis of face milling tool. The final results are also compared with standard bench mark algorithm i.e., Artificial Neural Network (ANN).