CNN based tool monitoring system to predict life of cutting tool

In this study, we present tool wear prediction system to monitor the flank wear of a cutting tool by Machine Learning technique namely, Convolutional Neural Network (CNN). Experimentations were performed on mild steel components under dry cutting condition by carbide inserts as cutting tool. Images of cutting tool and turned component were taken at regular interval using an inverted microscope to measure the progression of flank wear and the corresponding image of component was noted. These images were used as an input to the CNN model that extract the features and classify cutting tool in one of the three wear class namely, low, medium and high. The result of the CNN training set was used to monitor the life of cutting tool and predict its remaining useful life. In this work which is first of its kind, the CNN model gives an accuracy of 87.26% to predict the remaining useful life of a cutting tool. In particular, the study exhibits that CNN method gives good response to the data in the form of images, when used as an indicator of tool wear classification in different classes.