Minimizing error in tool wear estimation using artificial neural network

Intelligent Tool Condition Monitoring (TCM) does need an effective strategy to estimate the tool wear in order to avoid the subsequent consequences on the dimensional accuracy and surface finish of the product. This paper highlights the multisensory approach for tool wear estimation through sensor fusion by using Artificial Neural Network (ANN). It also provides a sequential approach to minimize the error in tool wear estimation by illustrating the influence of ANN parameters such as stopping criterion, modes of training the network and adaptation of learning rate parameter using fuzzy logic on estimation.