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Research Papers

B-Spline Wavelet-Based Multiresolution Analysis of Surface Texture in End-Milling of Aluminum

[+] Author and Article Information
Yongqing Li

Department of Mechanical Engineering,University of Michiganyongqing@umich.edu

Jun Ni

Department of Mechanical Engineering,University of Michiganjunni@umich.edu

J. Manuf. Sci. Eng 133(1), 011014 (Feb 18, 2011) (11 pages) doi:10.1115/1.4002452 History: Received June 17, 2008; Revised April 25, 2010; Published February 18, 2011; Online February 18, 2011

Modern manufacturing requires close monitoring by correlating finer frequency regimes of the surface texture to process parameters. This paper presents a study of B-spline wavelet-based multiresolution analysis (MRA) for surface texture characterization and process parameter correlation in end-milling. Motivated by multiresolution curves in computer graphics, an initial B-spline surface is first interpolated to the measured points of the surface texture. With a B-spline wavelet transform, the initial surface is decomposed into higher-frequency details and lower-frequency approximations. By taking the reconstructed surface roughness, waviness, and form based on different frequency regimes as the responses, regression models are created by considering the feed rate, spindle speed, axial depth of cut, and tool wear as controllable variables. The case studies and comparisons with an International Organization for Standardization (ISO) Gaussian filter have demonstrated the effectiveness of the proposed B-spline wavelet-based MRA algorithm for a surface texture analysis and manufacturing process diagnostics.

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Copyright © 2011 by American Society of Mechanical Engineers
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References

Figures

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Figure 4

Three spot measurement

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Figure 5

B spot of slot 1 from Wyko measurement

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Figure 6

Surface texture decomposition with B-spline wavelet filters: (a) surface form, (b) surface waviness, and (c) surface roughness

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Figure 1

Hierarchy of multiresolution curve decomposition

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Figure 2

Hierarchy of multiresolution surface decomposition: (a) standard wavelet transform and (b) nonstandard wavelet transform

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Figure 3

Milled slots of the two-level factorial experiment design

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Figure 7

Surface texture decomposition with Gaussian filters: (a) surface waviness and (b) surface roughness

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Figure 8

Boxplots of the relationship between the surface texture and milling parameters

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Figure 9

Magnified photos of the new tool, normal tool, and slightly and severely worn tools

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Figure 10

Milled slots with different tools

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Figure 11

B spot of the slot with new tool

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Figure 12

Surface texture decomposition with B-spline wavelet filters: (a) surface form, (b) surface waviness, and (c) surface roughness

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Figure 13

Surface texture decomposition with Gaussian filters: (a) surface waviness and (b) surface roughness

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Figure 14

Boxplots of the relationship between surface texture and tool wears

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