Prediction Of Work Piece Vibration In Boring of Aisi 316 Steel Using Artificial Neural Network
Abstract- In this paper, vibration of work piece is studied in boring of AISI 316 steel with cemented carbide tool inserts. A
design of experiments was prepared with eighteen experiments with two levels of tool nose radius and three levels of feed
rate and cutting speed. Experiments were performed on CNC lathe to obtain data amplitude of work piece vibration velocity.
A new attempt is made with Laser Doppler Vibrometer (LDV) for online data acquisition of work piece vibration and a high-
speed Fast Fourier transform analyzer was used to process the Acousto Optic Emission signals obtained from LDV. A
multilayer feed forward artificial neural network (ANN) model was trained with the experimental data using back-
propagation algorithm. Further, the ANN was used to predict amplitude of work piece vibration. The predicted values were
compared with the collected experimental data and percentage error was computed. Less percentage of error was found
between the experimental and predicted values.