The modeling, analysis, and application of the in-process machining data for CNC machining

Abstract

As the Computer Numerical Control (CNC) machining is executing, a large volume of in-process machining data is generated simultaneously in the CNC system, such as the interpolation point, actual feed rate, and tracking error. As a comprehensive reflection of entire CNC machining process, those types of data can be utilized to analyze the machining process and evaluate the machining results, especially in case of machining abnormalities, e.g., surface machining defects, excessive machining error, and malfunction of machine tool. In this paper, a novel idea called Chromatographic Point Cloud of Interpolation (CPCI) is defined by firstly building a point cloud from interpolation points and then assigning a color to each point according to a certain in-process machining data (e.g., feed rate). In order that the machining process can be effectively analyzed from the CPCI, a set of methods is proposed to efficiently triangulate the point cloud of the CPCI and quantify the continuity of in-process machining data of the CPCI. Experiments on two surfaces validate the effectiveness and advantage of the proposed triangular surface reconstruction method as well as its application in eliminating the surface defects resulting from the discontinuous feed rate between adjacent Cutter Location (CL) curves.