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TECHNICAL PAPERS

Utilizing Diverse Feature Data for Reconstruction of Scanned Object as a Basis for Inspection

[+] Author and Article Information
A. Miropolsky

Laboratory for CAD and Lifecycle Engineering, Department of Mechanical Engineering,  Technion-Israel Institute of Technology, Haifa 32000, Israelmealexm@technion.ac.il

A. Fischer

Laboratory for CAD and Lifecycle Engineering, Department of Mechanical Engineering,  Technion-Israel Institute of Technology, Haifa 32000, Israelmeranath@technion.ac.il

J. Comput. Inf. Sci. Eng 7(3), 211-224 (Jul 11, 2007) (14 pages) doi:10.1115/1.2768370 History: Received September 30, 2006; Revised July 11, 2007

Inspection of machined objects is one of the most important quality control tasks in the manufacturing industry. Ideally, inspection processes should be able to work directly on scan point data. Scan data, however, are typically very large scale (i.e., many points), unorganized, noisy, and incomplete. Therefore, direct processing of scanned points is problematic. Many of these problems may be reduced if reconstruction methods exploit diverse scan data, that is, information about the properties of the scanned object. This paper describes this concept and proposes new methods for extraction and processing of diverse scan data: (1) extraction (detection of a scanned object’s sharp features by the sharp feature detection method) and (2) processing (scan data reduction by the geometric bilateral filter method). The proposed methods are applied directly on the scanned points and are completely automatic, fast, and straightforward to implement. Finally, this paper demonstrates the integration of the proposed methods into the computational inspection process.

Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 1

Analysis of the surface points: (a) point neighborhood; (b) neighborhood XZ section

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Figure 2

Decomposition of the local surface area: (a) the nearest neighborhood of the surface sharp edge point, (b) neighborhood XY section, (c) neighborhood YZ section, and (d) neighborhood XZ section

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Figure 3

18 neighborhood of the voxel

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Figure 13

Box (19,601 points): (a) reconstruction with mean filter (shrinkage); (b) reconstruction with GBF (no shrinkage)

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Figure 14

Mechanical part 1 (35,395 points): (a) estimated RSPs with detected features; (b) reconstructed from RSP mesh

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Figure 15

Mechanical part 2 (164,425 points): (a) estimated RSPs with detected features (coarse resolution), (b) estimated RSPs with detected features (fine resolution), (c) HSDM with detected sharp voxels, and (d) reconstruction from RSP mesh

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Figure 17

Aircraft (117,152 points): (a) HSDM with detected sharp voxels; (b) reconstructed from RSP mesh

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Figure 18

Blade (882,954 points): (a) reconstructed from RSP mesh; (b) mesh with detected features

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Figure 4

Sorting of scanned points: (a) cloud of scanned points; (b) HSDM

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Figure 5

Voxel neighbor configurations: ((a) and (b)) Sharp feature voxel configurations; (c) Nonsharp feature voxel configuration

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Figure 6

Feature classification: (a) surface boundary; (b) sharp edge; (c) sharp corner

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Figure 7

RSP definition: (a) RSP definition for different surface types; (b) thin feature; (c) interconnection of surfaces

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Figure 8

Image filtering: (a) Gauss filter; (b) bilateral filter

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Figure 9

Smoothing effect of low-pass filter: (a) reconstructed mesh; (b) mesh detail

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Figure 10

Similarity of radius vectors and surface normal

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Figure 11

Upgrading normal in concave case

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Figure 12

Proposed inspection approach

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Figure 16

Comparison of SFD method versus mesh-based detection: Mechanical part 1: (a) sharp edges detected on mesh with ordinary threshold technique; (b) sharp edges detected on scanned points with SFD method. Mechanical part 2: (c) sharp edges detected on mesh with ordinary threshold technique; (d) sharp edges detected on scanned points with the SFD method.

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