Research Papers

Extended Geometric Filter for Reconstruction as a Basis for Computational Inspection

[+] Author and Article Information
Alexander Miropolsky1

Center for Advanced Orthopaedic Studies, Beth Israel Deaconess Medical Center (BIDMC), Harvard Medical School, Boston, MA 02215amiropol@bidmc.harvard.edu

Anath Fischer

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


Corresponding author.

J. Manuf. Sci. Eng 131(5), 051001 (Sep 04, 2009) (8 pages) doi:10.1115/1.3207738 History: Received July 23, 2008; Revised July 07, 2009; Published September 04, 2009

The inspection of machined objects is one of the most important quality control tasks in the manufacturing industry. Contemporary scanning technologies have provided the impetus for the development of computational inspection methods, where the computer model of the manufactured object is reconstructed from the scan data, and then verified against its digital design model. Scan data, however, are typically very large scale (i.e., many points), unorganized, noisy, and incomplete. Therefore, reconstruction is problematic. To overcome the above problems the reconstruction methods may exploit diverse feature data, that is, diverse information about the properties of the scanned object. Based on this concept, the paper proposes a new method for denoising and reduction in scan data by extended geometric filter. The proposed method is applied directly on the scanned points and is automatic, fast, and straightforward to implement. The paper demonstrates the integration of the proposed method into the framework of the computational inspection process.

Copyright © 2009 by American Society of Mechanical Engineers
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Figure 5

Integration EGF into inspection

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Figure 6

Mechanical part 1 (35,395 points): (a) noisy point cloud (ϕ∼N(0,0.0625)), (b) reconstruction with SOLIDWORKS07 , and (c) reconstruction with EGF

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Figure 7

Mechanical part 2 (165,074 points): (a) noisy point cloud (ϕ∼N(0,0.0625)) and (b) reconstruction with EGF

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Figure 8

Drill (197,790 points): (a) cloud of scanned points and (b) reconstruction with EGF

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Figure 9

GrayLoc connector (460,592 points): (a) cloud of scanned points and (b) reconstruction with EGF

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Figure 1

Denoising method: (a) plane denoising and (b) sphere denoising

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Figure 2

RSP definition: (a) RSP definition for different surface types, (b) separate surfaces: thin feature, and (c) continuous surface: sharp feature

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Figure 3

Saddle point case

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Figure 4

Estimation of the representative surface normal nRSP



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