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Research Papers

Assessment of the Robustness of a Fixtureless Inspection Method for Nonrigid Parts Based on a Verification and Validation Approach

[+] Author and Article Information
Sasan Sattarpanah Karganroudi, Jean-Christophe Cuillière, Vincent François

Équipe de Recherche en Intégration
Cao-CAlcul (ÉRICCA),
Université du Québec à Trois-Rivières,
Trois-Rivières, QC G9A 5H7, Canada

Souheil-Antoine Tahan

Laboratoire d'ingénierie des produits,
procédés et systèmes (LIPPS),
École de Technologie Supérieure,
Montréal, QC H3C 1K3, Canada

Manuscript received October 7, 2016; final manuscript received December 22, 2017; published online February 13, 2018. Assoc. Editor: Amit Shukla.

J. Verif. Valid. Uncert 2(4), 041002 (Feb 13, 2018) (22 pages) Paper No: VVUQ-16-1027; doi: 10.1115/1.4038917 History: Received October 07, 2016; Revised December 22, 2017

The increasing practical use of computer-aided inspection (CAI) methods requires assessment of their robustness in different contexts. This can be done by quantitatively comparing estimated CAI results with actual measurements. The objective is comparing the magnitude and dimensions of defects as estimated by CAI with those of the nominal defects. This assessment is referred to as setting up a validation metric. In this work, a new validation metric is proposed in the case of a fixtureless inspection method for nonrigid parts. It is based on using a nonparametric statistical hypothesis test, namely the Kolmogorov–Smirnov (K–S) test. This metric is applied to an automatic fixtureless CAI method for nonrigid parts developed by our team. This fixtureless CAI method is based on calculating and filtering sample points that are used in a finite element nonrigid registration (FENR). Robustness of our CAI method is validated for the assessment of maximum amplitude, area, and distance distribution of defects. Typical parts from the aerospace industry are used for this validation and various levels of synthetic measurement noise are added to the scanned point cloud of these parts to assess the effect of noise on inspection results.

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Figures

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Fig. 1

A regular aerospace panel (a) in free-state and (b) constrained by fixing jigs on the inspection fixture [2]

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Fig. 2

(a) Three-dimensional view of the CAD model of a nonrigid aluminum panel and (b) the scan model featuring three bump defects

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Fig. 3

GNIF corresponding sample points (in black) are located in the center of colorful zones on the CAD and scan models

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Fig. 4

(a) All GNIF sample points inserted into the CAD mesh based on classical Delaunay method (red spots) and (b) automatic sample point filtration based on curvature and von Mises stress criteria and criteria (blue spots)

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Fig. 5

(a) The scanned part with the nominal dimensions of defects, (b) estimated and nominal maximum amplitude (Dimax) of defects (mm), and (c) estimated and nominal area (Ai shown as red zones) of defects (mm2)

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Fig. 6

Definition of maximum amplitude Dimax and area of a defect Ai

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Fig. 7

Flowchart of our automatic fixtureless CAI method

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Fig. 8

Defects are identified as red zones based on the tolerance value (0.4 mm): (a) for nominal defects and (b) for estimated defects

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Fig. 9

CDF for nominal and estimated defects for bump #1 and bump #2

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Fig. 10

Estimation of the distance distribution of a defect (a) nominal defect, (b) for an accurate inspection, (c) for an overestimated defect, and (d) for a badly estimated defect

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Fig. 11

(a) A noise-free scan mesh (b), (c), (d) scan meshes with synthetic noise with Gaussian distribution with zero mean value and standard deviation equal to (b) 0.01 mm, (c) 0.02 mm, and (d) 0.03 mm

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Fig. 12

Synthesis of validation cases with small free-state deformation

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Fig. 13

CAD model along with GD&T specification for part A (dimensions are in mm)

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Fig. 14

(a) Nominal defect distance distribution for part A with small (local) defects, comparison between the CAD and scan model of part A with small (local) defects and bending deformation as a distance distribution for (b) noise-free scan mesh, (c) noisy scan mesh with σ = 0.01 mm, (d) noisy scan mesh with σ = 0.02 mm, and (e) noisy scan mesh with σ = 0.03 mm

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Fig. 15

(a) The scanned part with the nominal dimensions of big (global) defect, (b) nominal defect distance distribution for part A with a big (global) defect, comparison between the CAD and scan model of part A with a big (global) defect and bending deformation as a distance distribution for (c) noise-free scan mesh, (d) noisy scan mesh with σ = 0.01 mm, (e) noisy scan mesh with σ = 0.02 mm, and (f) noisy scan mesh with σ = 0.03 mm

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Fig. 16

CAD model along with GD&T specification for part B (dimensions are in mm)

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Fig. 17

Side views of the CAD model for part B (in green) compared with scan data in a free-state (in brown) with (a) bending deformation and (b) torsion deformation

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Fig. 18

(a) Nominal defect distance distribution for part B with small (local) defects, comparison between the CAD and scan model with small (local) defects and bending deformation as a distance distribution for (b) noise-free scan mesh, (c) noisy scan mesh with σ = 0.01 mm, (d) noisy scan mesh with σ = 0.02 mm, and (e) noisy scan mesh with σ = 0.03 mm

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Fig. 19

(a) Nominal defect distance distribution for part B with small (local) defects, comparison between the CAD and scan model with small (local) defects and torsion deformation as a distance distribution for (b) noise-free scan mesh, (c) noisy scan mesh with σ = 0.01 mm, (d) noisy scan mesh with σ = 0.02 mm, and (e) noisy scan mesh with σ = 0.03 mm

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Fig. 20

(a) Nominal defect distance distribution for part B with a big (global) defect, comparison between the CAD and scan model with a big (global) defect and bending deformation as a distance distribution for (b) noise-free scan mesh, (c) noisy scan mesh with σ = 0.01 mm, (d) noisy scan mesh with σ = 0.02 mm, and (e) noisy scan mesh with σ = 0.03 mm

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Fig. 21

(a) Nominal defect distance distribution for part B with a big (global) defect, comparison between the CAD and scan model with a big (global) defect and torsion deformation as a distance distribution for (b) noise-free scan mesh, (c) noisy scan mesh with σ = 0.01 mm, (d) noisy scan mesh with σ = 0.02 mm, and (e) noisy scan mesh with σ = 0.03 mm

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Fig. 22

Error intervals for part B with respect to the increase of noise amplitude

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Fig. 23

Three-dimensional views of CAD model (in green) compared with scan data in a free-state (in brown) for part A with: (a) small bending deformation and (b) large bending deformation

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Fig. 24

(a) Nominal defect distance distribution for part A with small (local) defects, comparison between the CAD and scan model of part A with small (local) defects and large bending deformation as a distance distribution for (b) noise-free scan mesh, (c) noisy scan mesh with σ = 0.01 mm, (d) noisy scan mesh with σ = 0.02 mm, and (e) noisy scan mesh with σ = 0.03 mm

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Fig. 25

(a) Nominal defect distance distribution for part A with a global defect, comparison between the CAD and scan model of part A with a global defect and large bending deformation as a distance distribution for (b) noise-free scan mesh, (c) noisy scan mesh with σ = 0.01 mm, (d) noisy scan mesh with σ = 0.02 mm, and (e) noisy scan mesh with σ = 0.0 3 mm

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Fig. 26

Absolute error (in %) in the estimation of defects for part A for small versus large deformation

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