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Research Papers

A Multifeature Approach to Tool Wear Estimation Using 3D Workpiece Surface Texture Parameters

[+] Author and Article Information
Yi Liao

 University of Michigan, Ann Arbor, MI 48109yiliao@umich.edu

David A. Stephenson, Jun Ni

 University of Michigan, Ann Arbor, MI 48109

J. Manuf. Sci. Eng 132(6), 061008 (Nov 09, 2010) (7 pages) doi:10.1115/1.4002852 History: Received March 25, 2010; Revised October 20, 2010; Published November 09, 2010; Online November 09, 2010

This work presents a new way to determine the condition of a cutting tool based on 3D texture parameters of workpiece surface. Recently, a laser holographic interferometer has been developed to rapidly measure a large workpiece surface and generate a 3D surface height map with micron level accuracy. This technique enables online surface measurement for machined workpieces. By measuring and analyzing workpiece surface texture, the interaction between the tool’s cutting edges and the workpiece surface can be extracted as a spatial signature. It can then be used as a warning sign for tool change because the workpiece produced by a heavily worn tool exhibits more irregularities in its surface texture than that produced by a normal tool. Multiple texture parameters such as image intensity histogram distribution parameter, 3D peak-to-valley height, and 3D surface waviness parameter are employed to indicate the onset of severe tool wear. In this work, aluminum (Al308) and compacted graphite iron parts were machined by a polycrystalline diamond insert and a multiphase coated tungsten carbide insert, respectively. After that, multiple 3D surface texture features of workpieces samples under different phases of tool wear were analyzed in order to assess tool wear conditions. The experimental results verify that these surface texture features can be used as good indicators for online tool wear monitoring.

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Copyright © 2010 by American Society of Mechanical Engineers
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Figures

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Figure 1

Flank wear observed in cutting tools

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Figure 2

(a) New PCD tool and (b) PCD tool with flank wear

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Figure 3

Image intensity histograms of part 2 (machined by break-in wear tool), part 5 (machined by steady wear tool), and part 9 (machined by severe wear tool)

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Figure 4

Total number of nonzero probability categories of intensity histogram versus tool flank wear

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Figure 5

(a) 3D height map of the surface machined by new tool (part 1) and (b) 3D height map of the surface machined by worn tool (part 9). The region shown has a size of 40×50 mm2.

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Figure 6

Surface ten-point peak-to-valley height versus tool wear VB

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Figure 7

Waviness and roughness separation of a simulated surface

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Figure 8

2D Gaussian filter weighting function (λxc=λyc=0.8 mm)

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Figure 9

3D surface waviness parameter Swa values for nine Al308 sample parts versus tool flank wear VB

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Figure 10

(a) New carbide insert and (b) carbide insert with flank wear

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Figure 11

Tool flank wear of nine sample workpieces in experiment 1

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Figure 12

3D surface waviness parameter Swa values for nine CGI sample parts versus tool flank wear VB in experiment 1

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Figure 13

3D surface waviness parameter Swa values for 19 CGI sample parts versus tool flank wear VB in experiment 2

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