An Experimental Model for the Prediction of Chip Thickness in Steel Turning

Abstract

In this research paper, a statistical exponential model was prepared for the determination of chip thickness during turning process of alloy steel by response surface methodology (RSM) with design of experiment method. The relationship between the chip thickness and machining conditions were analyzed. In the prediction of predictive models, cutting speed, feed rate, depth of cut and tool geometry (effective tool nose radius) were considered as input model variables and chip thickness was considered as response variable in the output form. The determined statistical model shows that the feed rate is the main influencing factor on chip thickness followed by tool nose radius and depth of cut. It increases with increase in feed rate but decreases with increase in cutting velocity and tool nose radius, respectively. The predicted values were found similar to the actual values.