Wear monitoring solution for end mills using deep learning and mobile application

Mobile computing can effectively monitor tool life by establishing a relationship between periodically captured cutting tool images and the usefulness level of an end mill. This work presents an image-based deep-learning model to estimate end mill wear parameters and provide tool state feedback to the machine operator. The algorithm estimates the Remaining Useful Life (RUL) and tool wear state from the mobile camera images, viz. initial, intermediate, and worn. The operator captures cutting tool images at predefined intervals on the machine using a mobile camera, macro lens, and tripod arrangement. A pre-trained network, GoogLeNet, is employed for feature extraction, linear regression, and recognizing the tool wear status as RUL. A mobile application is developed to display the wear state and RUL for assisting machine operators in replacing/regrinding decisions. The accuracy and robustness of the proposed model are demonstrated using RMSE (Root Mean Square Error) and Correlation Coefficient (R2) metrics. A set of machining experiments are performed, and it has been shown that the developed module can capture wear states and RUL for end mills. The proposed solutions can be deployed effectively by manufacturing industries for obtaining tool wear information without significant investment in machine vision hardware and software.