Diagnosis of tapping operations using an AI approach

An artificial neural network was utilized for the diagnosis of tapping operations. The input vector for the neural network is obtained by processing the signals of the thrust, torque, and lateral forces during the tapping operations. A total of ten indices was used in the input layer of the network. The output of the artificial neural network provides the tapping states. Five different tapping states were investigated: normal operation, tap wear, misalignment, oversize hole, and undersize hole. Experimental results showed that diagnosis of tapping operations through an artificial neural network can reach a success rate of over 95%.<
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