Tool condition monitoring in drilling using artificial neural networks

In modern day production, tool condition monitoring systems are needed to get better quality of jobs and to ensure reduction in the downtime of machine tools due to catastrophic tool failures. Tool condition monitors alert the operator about excessive tool wear and stop the machine in case of an impending breakage or collision of tool. A tool condition monitoring system based on measurement of thrust has been developed for a CNC gantry-drilling machine. The system, though performing well, has limitations due to its total dependence on single sensor input. In view of this, investigations have been carried out to adopt a multi-sensor approach for this system. The inputs of axial thrust, spindle motor current and vibrations are used and the decision regarding the condition of tool is made using Artificial Neural Networks. Initially, a training algorithm is used to learn the complex association between sensor inputs and drill wear. Later on the trained network is employed to assess the condition of drill on new sensory information. An Artificial Neural Network based on Error Back propagation algorithm is employed. The paper discusses various aspects considered in choosing the design parameters for the Neural Network. The experimental results are presented in the paper.