Abstract
Condition monitoring of the machining process is very important in today's precision manufacturing, especially in the case of reaming where in-process measurement of surface quality is difficult. In this paper, a new approach is presented for the condition monitoring in reaming using Artificial Neural Network. Acoustic emission, cutting force and vibration sensor data were measured during reaming operation and a multi-layer neural network was trained using these data with the conventional Back Propagation Algorithm for weight updation. Using force, vibration and acoustic emission parameters as input and Ra value of roughness, roundness error and residual stress as output, the network gave much superior results with sensor fusion.
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