Construction and validation of a surface topography prediction model for milling with double-arc milling cutter

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Authors: Mengchao Fan, Zhilong Zhou, Shiliang Wei, Wenhao Song, Emre Altaş
Cited by: 3
References: 26

Abstract

Abstract Addressing the limited research on surface topography modeling for non-conventional tools with diverse geometries, a method for constructing a surface topography prediction model for double-arc milling cutter(DAMC) is proposed. By innovatively segmenting the cutting edge, this method incorporates the double-arc cutting edge into the Z-map height field calculation, while simultaneously accounting for the inclination angle and cutting parameters. Integrating the Z-MAP method with the actual milling process of the DAMC enables a geometric description of the tool-workpiece contact. By discretizing the workpiece surface and dynamically updating the grid point heights to simulate the machining process, the proposed method enables the accurate calculation of the three-dimensional topography. Experiments were conducted with two types of DAMC. Results demonstrate high consistency between simulations and experiments in terms of three-dimensional topography distribution, two-dimensional profile curves, and surface roughness S a , with an average prediction error of 7.32% for S a . Analysis shows that the feed per tooth f z is significantly positively correlated with S a , and when f z increases from 0.1 mm/z to 0.133 mm/z, S a increases significantly. There is an optimal range for the inclination angle α . This study provides a theoretical basis for the selection and optimization of DAMC tool parameters and cutting parameters, important guidance for improving surface quality, and important theoretical basis for studying the surface topography of non-conventional tools.

Fields of Study & Subjects

Advanced machining processes and optimization Tribology and Lubrication Engineering Advanced Numerical Analysis Techniques

Publication details

Journal / source
Surface Topography Metrology and Properties
Publisher
IOP Publishing
Volume
14
Issue
1
Pages
015019–015019
Publication date
1 March 2026