Abstract
5-axis flank milling is applied extensively in aerospace, die-molds, and automotive industries because of high efficient material removal rate. Commercial CAM software can only simulate the tool path and collision at present, but cannot handle with cutting force during cutting process due to the variable geometrical cutter workpiece engagement (CWE) region of 5-axis milling. This paper presents a novel solid analytical model for extracting the CWE maps. The CWE is obtained analytically by performing Boolean operations between the cutter and in-process-workpiece (IPW) at any given cutter location (CL) point, instead of using the cutter and the removal volume. The proposed process simulation method could identify the CWE efficiently and precisely for general cutting tools. Finally, the CWE boundaries are mapped from a 3D space into a 2D plane defined by the immersion angle and the axial depth of a given cutter. The proposed solution can be easily integrated into the CAM software for predicting milling force and optimizing parameters in 5-axis milling.
Highlights
New processes in today's technology, e.g. electrochemical machining and 3D printing have been applied to manufacturing
This paper presents a novel solid analytical model for extracting the cutter workpiece engagement (CWE) maps
To compute the CWE area at a given cutter location (CL) point, the cutter body is intersected with some slicing planes which results in arcs or rings at each height increment in the tool coordinate system (TCS)
Summary
New processes in today's technology, e.g. electrochemical machining and 3D printing have been applied to manufacturing. 5-axis milling is quite often used for surface machining of impellers, but it still has some defects that need to be optimized. The tool paths and CL file can be generated by commercial CAM These solutions are only geometrical motion processes, namely, the kinematics properties of the machine tool and the cutting force between cutter and workpiece are not considered for the metal cutting process [1], so products are prone to quality problems. Erdim et al [4] proposed a new approach to shape representation called composite adaptively sampled distance fields. This function yields the minimum Euclidean distance from a point to the closest in the boundary of the set. These could be categorized into three major approaches: discrete representations (based on z-buffer, dexels or voxels), solid modeling (Brep, CSG), and point based methods [5]
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