Abstract

Ti-6Al-4V is difficult to machine material. It is widely used in a number of applications like aerospace industry,
marine application, petroleum refining, surgical implantation, chemical processing, food processing, and
electrochemical and its surface roughness is important. In this paper, surface roughness obtained from high-
speed turning of Ti-6Al-4V machined using uncoated carbide inserts have been evaluated using vibration signals.
Vibration signals have been analyzed using time domain and time-frequency domain. Vibration amplitude
measured in the cutting speed direction has been used in developing an artificial neural networks (ANNs) model
in time domain. Further, in the time-frequency domain, wavelet packet transform has been used to extract features
from the vibration signal in the cutting speed direction and has been used in developing another ANN model
to evaluate the surface roughness in terms of Ra, which is a widely used parameter. Multilayer perceptron has
been used for model development. Levenberg-Marquardt algorithm has been used for training the model. It has
been found that the time-frequency domain features extracted from the vibration signals are effective in surface
roughness evaluation, as the ANN model has given a prediction accuracy of 93% on test data.