Prediction of tool deflection using image processing in ball-end milling

Abstract

In milling, some of the factors that contribute to the poor quality of products are the cutting forces. Depending on the machining parameters, the cutting forces may significantly affect the tool being used in the machining process. Tool deflection can be modeled as bending deformation. Tool deflection causes poor surface quality, geometrical and dimensional errors. For this reason, it must be addressed during milling and reduced by changing the machining parameters. In the determination of tool deflection, force-based analytical and finite element methods (FEM) and sensor measurement methods are widely used. These technologies have drawbacks such as not being able to obtain fast data, being expensive, demanding precise control, and requiring continual calibration. This study aims to determine the deflection of the tool by image processing dependent on the tool/material pair and machining parameters in the milling process. For this purpose, the AL7075 material with a free-form surface was machined on a CNC milling machine. A mathematical equation is proposed to estimate the tool deflection based on the image processing results. The method has shown that tool deviation can be detected more quickly and simply by image processing.