In a free state, flexible parts may have different shapes compared to their computer-aided design (CAD) model. Such parts may likewise undergo large deformations depending on their space orientation. These conditions severely restrict the feasibility of inspecting flexible parts without restricting the deformations of the part and therefore require dedicated and expensive tools such as a conformation jig or a fixture to maintain the integrity of the part. To address these challenges, this paper proposes a new inspection method, the iterative displacement inspection (IDI) algorithm, that evaluates profile variations without the need for specialized fixtures. This study examines 32 models of simulated manufactured parts to show that the IDI algorithm can iteratively deform the meshed CAD model until it resembles the scanned manufactured part, which enables their comparison. The method deforms the mesh in such a manner so as to ensure its smoothness. This way, neither surface defects nor the measurement noise of the scanned parts are concealed during the matching process. As a result, the case studies illustrate that the method’s error essentially only represents the scanned part’s measurement noise. The inspection results, therefore, solely reflect the effect of variations from the manufacturing process itself and not the deformation of the part.

1.
Savio
,
E.
,
Chiffre
,
L. D.
, and
Schmitt
,
R.
, 2007, “
Metrology of Freeform Shaped Parts
,”
CIRP Ann.
0007-8506,
56
(
2
), pp.
810
835
.
2.
Gu
,
P.
, and
Huang
,
X.
, 1998, “
CAD-Model Based Inspection of Sculptured Surfaces With Datums
,”
Int. J. Prod. Res.
0020-7543,
36
(
5
), pp.
1351
1367
.
3.
Li
,
Y.
, and
Gu
,
P.
, 2004, “
Free-Form Surface Inspection Techniques State of the Art Review
,”
Comput.-Aided Des.
0010-4485,
36
(
13
), pp.
1395
1417
.
4.
Li
,
Y.
, and
Gu
,
P.
, 2005, “
Inspection of Free-Form Shaped Parts
,”
Rob. Comput.-Integr. Manufact.
0736-5845,
21
(
4–5
), pp.
421
430
.
5.
Yao
,
A. W. L.
, 2005, “
Applications of 3D Scanning and Reverse Engineering Techniques for Quality Control of Quick Response Products
,”
Int. J. Adv. Manuf. Technol.
0268-3768,
26
(
11–12
), pp.
1284
1288
.
6.
Gao
,
J.
,
Gindy
,
N.
, and
Chen
,
X.
, 2006, “
An Automated GD&T Inspection System Based on Non-Contact 3D Digitization
,”
Int. J. Prod. Res.
0020-7543,
44
(
1
), pp.
117
134
.
7.
Shi
,
Q.
, and
Xi
,
N.
, 2008, “
Automated Data Processing for a Rapid 3D Surface Inspection System
,”
IEEE International Conference on Robotics and Automation 2008 (ICRA 2008)
, Pasadena, CA, pp.
3939
3944
.
8.
Ravishankar
,
S.
,
Dutt
,
H.
, and
Gurumoorthy
,
B.
, 2010, “
Automated Inspection of Aircraft Parts Using a Modified ICP Algorithm
,”
Int. J. Adv. Manuf. Technol.
0268-3768,
46
(
1–4
), pp.
227
236
.
9.
Karbacher
,
S.
,
Babst
,
J.
,
Häusler
,
G.
, and
Laboureux
,
X.
, 1999, “
Visualization and Detection of Small Defects on Car-Bodies
,”
Modeling and Visualization ‘99
, Sankt Augustin, pp.
1
8
.
10.
Leopold
,
J.
,
Günther
,
H.
, and
Leopold
,
R.
, 2003, “
New Developments in Fast 3D-Surface Quality Control
,”
Measurement
0263-2241,
33
(
2
), pp.
179
187
.
11.
Eichhorn
,
A.
,
Girimonte
,
D.
,
Klose
,
A.
, and
Kruse
,
R.
, 2005, “
Soft Computing for Automated Surface Quality Analysis of Exterior Car Body Panels
,”
Appl. Soft Comput.
1568-4946,
5
(
3
), pp.
301
313
.
12.
Dorïng
,
C.
,
Eichhorn
,
A.
,
Xiaomeng
,
W.
, and
Kruse
,
R.
, 2006, “
Improved Classification of Surface Defects for Quality Control of Car Body Panels
,”
2006 IEEE International Conference on Fuzzy Systems
, Vancouver, BC, Canada, pp.
1476
1481
.
13.
Megahed
,
F.
, and
Camelio
,
J.
, 2010, “
Real-Time Fault Detection in Manufacturing Environments Using Face Recognition Techniques
,”
J. Intell. Manuf.
, online first.
14.
Besl
,
P. J.
, and
McKay
,
H. D.
, 1992, “
A Method for Registration of 3-D Shapes
,”
IEEE Trans. Pattern Anal. Mach. Intell.
0162-8828,
14
(
2
), pp.
239
256
.
15.
Masuda
,
T.
, and
Yokoya
,
N.
, 1994, “
A Robust Method for Registration and Segmentation of Multiple Range Images
,”
Proceedings of the 1994 Second CAD-Based Vision Workshop
, Champion, PA, pp.
106
113
.
16.
Park
,
S. Y.
, and
Murali
,
S.
, 2003, “
A Fast Point-to-Tangent Plane Technique for Multi-View Registration
,”
Proceedings of the Fourth International Conference on 3-D Digital Imaging and Modeling (3DIM 2003)
, Banff, AB, Canada, pp.
276
283
.
17.
Pottman
,
H.
, and
Hofer
,
M.
, 2002, “
Geometry of the Squared Distance Function to Curves and Surfaces
,” Institute of Geometry, Vienna University of Technology, Technical Report No. 90.
18.
Rusinkiewicz
,
S.
, and
Levoy
,
M.
, 2001, “
Efficient Variants of the ICP Algorithm
,”
Proceedings of the Third International Conference on 3-D Digital Imaging and Modeling (3DIM 2001)
, Quebec City, QC, Canada, pp.
145
152
.
19.
Gelfand
,
N.
,
Ikemoto
,
L.
,
Rusinkiewicz
,
S.
, and
Levoy
,
M.
, 2003, “
Geometrically Stable Sampling for the ICP Algorithm
,”
Proceedings of the Fourth International Conference on 3-D Digital Imaging and Modeling (3DIM 2003)
, Banff, AB, Canada, pp.
260
267
.
20.
Dawant
,
B. M.
, 2002, “
Non-Rigid Registration of Medical Images: Purpose and Methods, A Short Survey
,”
2002 IEEE International Symposium on Biomedical Imaging
, Washington, DC, p.
4
.
21.
Holden
,
M.
, 2008, “
A Review of Geometric Transformations for Nonrigid Body Registration
,”
IEEE Trans. Med. Imaging
0278-0062,
27
(
1
), pp.
111
128
.
22.
Ferrant
,
M.
,
Warfield
,
S. K.
,
Guttmann
,
C. R. G.
,
Vulkern
,
R. V.
,
Jolesz
,
F. A.
, and
Kikinis
,
R.
, 1999, “
3D Image Matching Using a Finite Element Based Elastic Deformation Model
,”
Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention—MICCAI ‘99
, Cambridge, UK, Vol.
1679
, pp.
202
209
.
23.
Feldmar
,
J.
, and
Ayache
,
N.
, 1994, “
Rigid and Affine Registration of Smooth Surfaces Using Differential Properties
,”
Proceedings of the Third European Conference on Computer Vision
, Stockholm, Sweden, Vol.
2
, p.
397
.
24.
Feldmar
,
J.
, and
Ayache
,
N.
, 1996, “
Rigid, Affine and Locally Affine Registration of Free-Form Surfaces
,”
Int. J. Comput. Vis.
0920-5691,
18
(
2
), pp.
99
119
.
25.
Allen
,
B.
,
Curless
,
B.
, and
Popovic
,
Z.
, 2003, “
The Space of Human Body Shapes: Reconstruction and Parameterization From Range Scans
,”
ACM SIGGRAPH 2003
, San Diego, CA, Vol.
22
, pp.
587
594
.
26.
Amberg
,
B.
,
Romdhani
,
S.
, and
Vetter
,
T.
, 2007, “
Optimal Step Nonrigid ICP Algorithms for Surface Registration
,”
IEEE Conference on Computer Vision and Pattern Recognition (CVPR ‘07)
, Minneapolis, MN, pp.
1
8
.
27.
Liu
,
S. C.
,
Hu
,
S. J.
, and
Woo
,
T. C.
, 1996, “
Tolerance Analysis for Sheet Metal Assemblies
,”
ASME J. Mech. Des.
0161-8458,
118
(
1
), pp.
62
67
.
28.
Camelio
,
J. A.
,
Hu
,
S. J.
, and
Ceglarek
,
D.
, 2004, “
Impact of Fixture Design on Sheet Metal Assembly Variation
,”
J. Manuf. Syst.
0278-6125,
23
(
3
), pp.
182
193
.
29.
Liu
,
S. C.
, and
Hu
,
S. J.
, 1997, “
Variation Simulation for Deformable Sheet Metal Assemblies Using Finite Element Methods
,”
ASME J. Manuf. Sci. Eng.
1087-1357,
119
(
3
), pp.
368
373
.
30.
Merkley
,
K. G.
, 1998, “
Tolerance Analysis of Compliant Assemblies
,” Ph.D. thesis, Department of Mechanical Engineering, Brigham Young University, Provo, UT.
31.
Bihlmaier
,
B. F.
, 1999, “
Tolerance Analysis of Flexible Assemblies Using Finite Element and Spectral Analysis
,” Ph.D. thesis, Department of Mechanical Engineering, Brigham Young University, Provo, UT.
32.
Markvoort
,
L.
,
Gliniorz
,
S.
, and
Deneux
,
D.
, 2005, “
Prise en compte de la nature flexible des matériaux pour l’analyse de tolérance
,”
9ième Colloque National AIP PRIMECA
.
33.
Markvoort
,
L.
,
Gliniorz
,
S.
, and
Deneux
,
D.
, 2005, “
Etat de l’art des méthodes d’analyse de tolérance
,”
9ième Colloque National AIP PRIMECA
.
34.
Markvoort
,
L.
, 2007, “
Méthodologie d’analyse statistique de tolérances dans les assemblages impliquant des composantes déformables
,” Ph.D. thesis, Université de Valenciennes et du Hainaut-Cambrésis, Valenciennes, France.
35.
Barber
,
C. B.
,
Dobkin
,
D. P.
, and
Huhdanpaa
,
H.
, 1996, “
The Quickhull Algorithm for Convex Hulls
,”
ACM Trans. Math. Softw.
0098-3500,
22
(
4
), pp.
469
483
.
36.
Gonzalez
,
R. C.
, and
Woods
,
R. E.
, 2008,
Digital Image Processing
, 3rd ed.,
Prentice-Hall
,
Upper Saddle River, NJ
.
37.
Pewsey
,
A.
, 2004, “
Improved Likelihood Based Inference for the General Half-Normal Distribution
,”
Commun. Stat: Theory Meth.
0361-0926,
33
(
2
), pp.
197
204
.
38.
Bland
,
J.
, 2005, “
The Half-Normal Distribution Method for Measurement Error: Two Case Studies
,” Department of Health Sciences, University of York, Technical Report.
39.
Jirka
,
T.
, and
Skala
,
V.
, 2002, “
Gradient Vector Estimation and Vertex Normal Computation
,” Department of Computer Science and Engineering, University of West Bohemia, Pilsen, Czech Republic, Technical Report No. DCSE/TR-2002-08.
You do not currently have access to this content.