Modeling of burr thickness in micro-end milling of Ti6Al4V

Abstract

Mechanical micro-machining of Ti6Al4V is finding great demand because of its wide range of application in various fields such as communication, optics and biomedical devices. Increasing demands on functioning and performance requires components to be free from burrs after the machining process. Presence of burrs on micro-mechanical parts or features significantly affects quality and proper assembly of the parts. Also in micro-machining, the size of burr is comparable to that of micro-features. Since the formation of burr is inevitable in any machining process, generally the deburring operation is performed to remove burrs. Burr thickness is one of the important parameters which describe the time and method necessary for the deburring operation. Burrs on micro-parts are generally characterized using scanning electron microscope, which is a time-consuming, costly and non-value-added activity. However, a proper mathematical model will help predict burr thickness easily. In this article, a mathematical model to predict burr thickness during micro-end milling of Ti6Al4V is presented. The proposed model was developed based on the principle of continuity of work at the transition from chip formation to burr formation. Ti6Al4V titanium alloy is one of the materials which generates segmented (saw-tooth) chips at low cutting speeds. Hence, initially an appropriate material constitutive model was selected based on better prediction of burr thickness. Then, to reduce the prediction error, machining temperature was evaluated for all experimental conditions and included in the model. From the initial study, it was found that Hyperbolic TANgent material model gives a better prediction compared to Johnson–Cook material model. Later, after including machining temperature into the model it was observed that the prediction error was reduced. The proposed model was validated with the experimental results.