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Research Papers

Analytical Topography Simulation of Micro/Nano Textures Generated on Freeform Surfaces in Double-Frequency Elliptical Vibration Cutting Based Diamond Turning

[+] Author and Article Information
Chengming Zuo, Qiang Liu, Rongqi Wang, Pengzi Xu, Xu Zhang

School of Mechanical Science and Engineering,
Jilin University,
Changchun 130022, China

Xiaoqin Zhou

School of Mechanical Science and Engineering,
Jilin University,
Changchun 130022, China
e-mail: xqzhou@jlu.edu.cn

Jieqiong Lin

School of Electro-Mechanical Engineering,
Changchun University of Technology,
Changchun 130012, China

1Corresponding author.

Manuscript received October 31, 2017; final manuscript received June 20, 2018; published online July 27, 2018. Assoc. Editor: Laine Mears.

J. Manuf. Sci. Eng 140(10), 101010 (Jul 27, 2018) (14 pages) Paper No: MANU-17-1675; doi: 10.1115/1.4040616 History: Received October 31, 2017; Revised June 20, 2018

The surfaces with textures have been widely used as functional surfaces, and the textures are usually generated on flat or cylindrical surfaces. Textured freeform surfaces will have more potential applications. The authors have proposed the double-frequency elliptical vibration cutting (DFEVC) method to machine freeform surfaces on steel materials. Based on this method, a new diamond turning method is developed, in which the variable-frequency modulations are utilized to control the tool marks left on the machined surface to generate the micro/nano dimple textures with high uniformity on the freeform surface. Different from the conventional surface topography model based on the ideal tool cutting edge with zero cutting edge radius, a new modeling approach based on the tool surface profiles is proposed, in which the rake face, the flank face, and the cutting edge surface with actual non-zero cutting edge radius instead of the ideal cutting edge are included for the tool model, the tool surfaces during the machining process are analytically described as a function of the tool geometry and the machining parameters, and the influences of the tool surface profiles on the topography generation of the machined surface are considered. A typical freeform surface is textured on die steel, and the measured results verify the feasibility of the proposed turning method. Compared with the topography prediction results based on the ideal cutting edge, the results considering the tool surfaces show improved simulation accuracy, and are consistent with the experimental results, which validates the proposed topography prediction approach.

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Figures

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Fig. 1

Illustration of the DFEVC process in diamond turning using variable-frequency modulations

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Fig. 2

Textured surfaces at different positions

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Fig. 3

Tool motion projection and tool nose radius compensation: (a) projection of the tool motions to the XOY plane at time t, and (b) tool nose radius compensation in the rake plane XRORZR

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Fig. 4

Tool geometry model

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Fig. 5

Surface topography generation processes: (a) surface generation by the cutting edge surface in the mth revolution, (b) surface generation by the flank face in the mth revolution, and (c) surface generation by the cutting edge surface in the m + 1th revolution, and (d) surface generation by the flank face in the m + 1th revolution

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Fig. 6

Experimental setup

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Fig. 7

Machined sinusoidal wavy surface and predicted topography of the textured surface

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Fig. 8

Measured and simulated results of area G marked in Fig. 7(b): (a) measured results of area G, (b) simulated results of area G based on conventional model, (c) simulated results of area G based on cutting edge surface profile, and (d) simulated results of area G based on cutting edge surface and flank surface profiles

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Fig. 9

Experimental and predicted results of the different areas marked in Fig. 7(b): (a) measured and simulated results of area A, (b) measured and simulated results of area B, (c) measured and simulated results of area C, (d) measured and simulated results of area D, (e) measured and simulated results of area E, and (f) measured and simulated results of area F

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Fig. 10

Comparisons of the surface profiles between the simulated and the measured results along the feed and the cutting directions: (a) predicted and measured surface profiles of area C, (b) predicted and measured surface profiles of area D, (c) predicted and measured surface profiles of area E, (d) predicted and measured surface profiles of area F, and (e) predicted and measured surface profiles of area G

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