The Burrs Formation Prediction and Minimization Based on the Optimal Cutting Parameters Design Method

Abstract

Researches have shown the cutting parameters and the burrs formation are close related, but these relationships cannot be formed in a simple formula. The mechanism of burrs formation and its appearance occurred to the cutting material quite depends on the method of machining and cutting condition. Although the relationships are nonlinear, the residual burrs can be reduced significantly by selecting appropriate cutting parameters during machining. In this research, a series of cutting experiments that based on Taguchi experimental method has been conducted to explore the formation of burrs size and types under different cutting conditions. The relationship of cutting parameters and the burrs formation data are collected for further study. With the burr size as the evaluation index, cutting speed, feed-rate, and depth of cut are chosen to cut the medium carbon steel (S50C). The Taguchi Method and Artificial Neural Network are adapted to establish the burrs formation model, and then the neural network based on optimal design method as a tool in cutting parameters optimization is employed. The result shows the goal of reduce burrs size into a reasonable region can be accomplished by adjusting cutting parameters. The experiments proved the burrs size with the optimal design method can be reduced as much as 67 to 78% that comparing with experienced cutting condition. As this point of view, the parameters optimization operations by optimal parameters design method offer an effective tool to reduce the burrs size in machining.