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Prediction of quadratic cylinder surface morphology for ultrasonic assisted polishing.

Authors :
Meng, Fanwei
Cui, Zhijie
Qu, Sheng
Liang, Yingdong
Ma, Zhelun
Wang, Zixuan
Liu, Yixuan
Yu, Tianbiao
Zhao, Ji
Source :
Measurement (02632241). Feb2024, Vol. 225, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

• A model of material removal for axial ultrasonic assisted polishing of spherical polishing heads was developed. • The mechanism of trajectory generation for polishing tools in quadratic cylinder surfaces was presented. • An innovative ultrasonic assisted surface morphology prediction model was established based on material removal models and surface trajectories. • Two dimensional power spectral density and roughness analyses were performed on the experimental results. Aluminum alloys with light weight and excellent performance in processing are widely used in different kinds of optical reflector elements. In this paper, An innovative model for predicting the surface morphology of ultrasonic assisted polishing quadratic cylinder surfaces was developed based on the material removal model and calculation of the trajectory methods. Experimental verification was accomplished. By analyzing the simulation and experimental surface roughness analysis and two-dimensional power spectral density, the maximum error between the roughness experiment and the simulation is 9.3%, and the maximum error between the peak two dimensional power spectral density of experimental and simulation results is 9.8%. Surface properties can be enhanced and errors of medium and high frequency in the polished surface region can be diminished with the ultrasonic assisted polishing. This study provides important guidance for ultrasonic assisted polishing of different types of quadratic cylinder surfaces including without limitation cylindrical, elliptic, and parabolic surfaces. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02632241
Volume :
225
Database :
Academic Search Index
Journal :
Measurement (02632241)
Publication Type :
Academic Journal
Accession number :
175027417
Full Text :
https://doi.org/10.1016/j.measurement.2023.114025