Production of Cutting Tool Vibration in Turning Using Artificial Neural Network

Abstract

The cutting parameters like cutting speed, feed, and depth of cut can affect the tool life. Also, the vibration of the tool can affect the tool life. The aim of this study is to predict the cutting tool vibration to enhance the tool life. In this paper, vibration of tool is studied with HSS tool (high speed steel) while turning mild steel. The tool vibration is measured using X Viber vibration meter. The experimental data are tabulated and imported to artificial neural network (ANN). A multilayer perceptron model is trained with back-propagation algorithm using the experimental data. The rake angle, cutting speed, and feed are considered as input parameters for training the ANN. The trained ANN is used to predict tool vibration for different conditions. The predicted values are compared for the specific range to justify the tool change.