In this paper, we present multiple methods to detect fasteners (bolts, screws, and nuts) from tessellated mechanical assembly models. There is a need to detect these geometries in tessellated formats because of features that are lost during the conversions from other geometry representations to tessellation. Two geometry-based algorithms, projected thread detector (PTD) and helix detector (HD), and four machine learning classifiers, voted perceptron (VP), Naïve Bayes (NB), linear discriminant analysis, and Gaussian process (GP), are implemented to detect fasteners. These six methods are compared and contrasted to arrive at an understanding of how to best perform this detection in practice on large assemblies. Furthermore, the degree of certainty of the automatic detection is also developed and examined so that a user may be queried when the automatic detection leads to a low certainty in the classification. This certainty measure is developed with three probabilistic classifier approaches and one fuzzy logic-based method. Finally, once the fasteners are detected, the authors show how the thread angle, the number of threads, the length, and major and root diameters can be determined. All of the mentioned methods are implemented and compared in this paper. A proposed combination of methods leads to an accurate and robust approach of performing fastener detection.

References

References
1.
Weber
,
A.
,
2012
, “
Comeback of the American Fastener
,”
Assembly Magazine
, Troy, MI.https://www.assemblymag.com/articles/90042-comeback-of-the-american-fastener
2.
El Camino College,
2003
,“Fasteners in the Aerospace Industry: Aerospace Fastener Applications, Part 1,” Lecture Notes, El Camino College, Torrance, CA.
3.
Brady, C.
, 2017, “
The Boeing 737 Technical Guide
,” Tech Pilot Services Ltd., Cheshire, UK.
4.
Rafibakhsh
,
N.
, and
Campbell
,
M. I.
,
2015
, “Hierarchical Primitive Surface Classification From Triangulated Solids for Defining Part-to-Part Degrees of Freedom,”
ASME
Paper No. DETC2015-46069.
5.
Andreopoulos
,
A.
, and
Tsotsos
,
J. K.
,
2013
, “
50 Years of Object Recognition: Directions Forward
,”
Comput. Vision Image Understanding
,
117
(
8
), pp.
827
891
.
6.
Mazzeo
,
P.
,
Nitti
,
M.
,
Stella
,
E.
, and
Distante
,
A.
,
2004
, “
Visual Recognition of Fastening Bolts for Railroad Maintenance
,”
Pattern Recognit. Lett.
,
25
(
6
), pp.
669
677
.
7.
Feng
,
H.
,
Jiang
,
Z.
,
Xie
,
F.
,
Yang
,
P.
,
Shi
,
J.
, and
Chen
,
L.
,
2014
, “
Automatic Fastener Classification and Defect Detection in Vision-Based Railway Inspection Systems
,”
IEEE Trans. Instrum. Meas.
,
63
(
4
), pp.
877
888
.
8.
Ohbuchi
,
R.
, and
Takei
,
T.
,
2003
, “
Shape Similarity Comparison of 3D Models Using Alpha Shapes
,” 11th
IEEE
Pacific Conference on Computer Graphics and Applications,
Canmore, AB, Canada, Oct. 8–10, pp.
293
302
.
9.
Tung
,
T.
, and
Schmitt
,
F.
,
2004
, “
Augmented Reeb Graphs for Content-Based Retrieval of 3D Mesh Models
,”
Shape Modeling Applications
, Genova, Italy, June 7–9, pp.
157
389
.
10.
Gal
,
R.
, and
Cohen-Or
,
D.
,
2006
, “
Salient Geometric Features for Partial Shape Matching and Similarity
,”
ACM Trans. Graphics
,
25
(
1
), pp.
130
150
.
11.
Vranic
,
D. V.
, and
Saupe
,
D.
,
2002
, “
Description of 3D-Shape Using a Complex Function on the Sphere
,”
IEEE International Conference on Multimedia and Expo
(
ICME
), Lausanne, Switzerland, Aug. 26–29, pp.
177
180
.
12.
Ohbuchi
,
R.
,
Otagiri
,
T.
,
Ibato
,
M.
, and
Takei
,
T.
,
2002
, “
Shape-Similarity Search of Three-Dimensional Models Using Parameterized Statistics
,” Tenth
IEEE
Pacific Conference on Computer Graphics and Applications,
Beijing, China, Oct. 9–11, pp.
265
274.
13.
Cuillière
,
J.-C.
,
François
,
V.
,
Souaissa
,
K.
,
Benamara
,
A.
, and
BelHadjSalah
,
H.
,
2011
, “
Automatic Comparison and Remeshing Applied to CAD Model Modification
,”
Comput. Des.
,
43
(
12
), pp.
1545
1560
.
14.
Chu
,
C.-H.
,
Cheng
,
H.-C.
,
Wang
,
E.
, and
Kim
,
Y.-S.
,
2009
, “
ANN-Based 3D Part Search With Different Levels of Detail (LOD) in Negative Feature Decomposition
,”
Expert Syst. Appl.
,
36
(
8
), pp.
10905
10913
.
15.
Cuillière
,
J.-C.
,
François
,
V.
,
Souaissa
,
K.
,
Benamara
,
A.
, and
BelHadjSalah
,
H.
,
2009
, “
Automatic CAD Models Comparison and Re-Meshing in the Context of Mechanical Design Optimization
,”
18th International Meshing Roundtable
, Salt Lake City, UT, Oct. 25–28, pp.
231
245
.http://www.imr.sandia.gov/papers/imr18/Cuilli%E8re.pdf
16.
Karnik
,
M. V.
,
Anand
,
D. K.
,
Eick
,
E.
,
Gupta
,
S. K.
, and
Kavetsky
,
R.
,
2013
, “
Integrated Visual and Geometric Search Tools for Locating Desired Parts in a Part Database
,”
Comput.-Aided Des. Appl.
,
2
(
6
), pp.
727
736
.
17.
You
,
C.-F.
,
Tsai
,
Y.-L.
, and
Liu
,
K.-Y.
,
2010
, “
Representation and Similarity Assessment in Case-Based Process Planning and Die Design for Manufacturing Automotive Panels
,”
Int. J. Adv. Manuf. Technol.
,
51
(
1–4
), pp.
297
310
.
18.
Karnik
,
M.
,
Gupta
,
S. K.
, and
Magrab
,
E. B.
,
2005
, “
Geometric Algorithms for Containment Analysis of Rotational Parts
,”
Comput. Des.
,
37
(
2
), pp.
213
230
.
19.
Deshmukh
,
A.
,
2006
, “
Content Based Search of Mechanical Assemblies
,”
Ph.D. thesis
,
University of Maryland, College Park, MD
.https://drum.lib.umd.edu/handle/1903/4140
20.
Deshmukh
,
A. S.
,
Gupta
,
S. K.
,
Karnik
,
M. V.
, and
Sriram
,
R. D.
,
2005
, “A System for Performing Content-Based Searches on a Database of Mechanical Assemblies,”
ASME
Paper No. IMECE2005-82342.
21.
Deshmukh
,
A. S.
,
Banerjee
,
A. G.
,
Gupta
,
S. K.
, and
Sriram
,
R. D.
,
2008
, “
Content-Based Assembly Search: A Step Towards Assembly Reuse
,”
Comput. Des.
,
40
(
2
), pp.
244
261
.
22.
Chu
,
C.-H.
, and
Hsu
,
Y.-C.
,
2006
, “
Similarity Assessment of 3D Mechanical Components for Design Reuse
,”
Rob. Comput. Integr. Manuf.
,
22
(
4
), pp.
332
341
.
23.
Iyer
,
N.
,
Jayanti
,
S.
,
Lou
,
K.
,
Kalyanaraman
,
Y.
, and
Ramani
,
K.
,
2004
, “
A Multi-Scale Hierarchical 3D Shape Representation for Similar Shape Retrieval
,”
Tools and Methods of Competitive Engineering
(
TMCE
), Lausanne, Switzerland, Apr. 12–16.http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.443.9849&rep=rep1&type=pdf
24.
Hong
,
T.
,
Lee
,
K.
, and
Kim
,
S.
,
2006
, “
Similarity Comparison of Mechanical Parts to Reuse Existing Designs
,”
Comput. Des.
,
38
(
9
), pp.
973
984
.
25.
Ip
,
C. Y.
,
Lapadat
,
D.
,
Sieger
,
L.
, and
Regli
,
W. C.
,
2002
, “
Using Shape Distributions to Compare Solid Models
,”
Seventh ACM Symposium on Solid Modeling and Applications
(
SMA
), Saarbrücken, Germany, June 17–21, pp.
273
280
.
26.
Cheng
,
H.-C.
,
Lo
,
C.-H.
,
Chu
,
C.-H.
, and
Kim
,
Y. S.
,
2011
, “
Shape Similarity Measurement for 3D Mechanical Part Using D2 Shape Distribution and Negative Feature Decomposition
,”
Comput. Ind.
,
62
(
3
), pp.
269
280
.
27.
Dini
,
G.
,
Failli
,
F.
,
Lazzerini
,
B.
, and
Marcelloni
,
F.
,
1999
, “
Generation of Optimized Assembly Sequences Using Genetic Algorithms
,”
CIRP Ann. Manuf. Technol.
,
48
(
1
), pp.
17
20
.
28.
Romney
,
B.
,
Godard
,
C.
,
Goldwasser
,
M.
, and
Ramkumar
,
G.
,
1995
, “
An Efficient System for Geometric Assembly Sequence Generation and Evaluation
,”
International Computers in Engineering
, pp.
699
712
.http://cs.slu.edu/~goldwasser/publications/ASME1995.pdf
29.
Caselli
,
S.
, and
Zanichelli
,
F.
,
1995
, “
On Assembly Sequence Planning Using Petri Nets
,”
IEEE
International Symposium on Assembly and Task Planning
, Pittsburgh, PA, Aug. 10–11, pp.
239
244
.
30.
Hong
,
D. S.
, and
Cho
,
H. S.
,
1997
, “
Generation of Robotic Assembly Sequences with Consideration of Line Balancing Using Simulated Annealing
,”
Robotica
,
15
(
6
), pp.
663
673
.
31.
Guan
,
Q.
,
Liu
,
J. H.
, and
Zhong
,
Y. F.
,
2002
, “
A Concurrent Hierarchical Evolution Approach to Assembly Process Planning
,”
Int. J. Prod. Res.
,
40
(
14
), pp.
3357
3374
.
32.
Miller
,
J. M.
, and
Hoffman
,
R. L.
,
1989
, “
Automatic Assembly Planning With Fasteners
,”
International Conference on Robotics and Automation
(
ICRA
), Scottsdale, AZ, May 14–19, pp.
69
74
.
33.
Jones
,
R. E.
,
Wilson
,
R. H.
, and
Calton
,
T. L.
,
1998
, “
On Constraints in Assembly Planning
,”
IEEE Trans. Rob. Autom.
,
14
(
6
), pp.
849
863
.
34.
Homem de Mello
,
L. S.
, and
Sanderson
,
A. C.
,
1991
, “
A Correct and Complete Algorithm for the Generation of Mechanical Assembly Sequences
,”
IEEE Trans. Rob. Autom.
,
7
(
2
), pp.
228
240
.
35.
Da Xu
,
L.
,
Wang
,
C.
,
Bi
,
Z.
, and
Yu
,
J.
,
2012
, “
AutoAssem: An Automated Assembly Planning System for Complex Products
,”
IEEE Trans. Ind. Inf.
,
8
(
3
), pp.
669
678
.
36.
Diaz-Calderon
,
A.
,
Navin-Chandra
,
D.
, and
Khosla
,
P. K.
,
1995
, “
Measuring the Difficulty of Assembly Tasks From Tool Access Information
,”
IEEE
International Symposium on Assembly and Task Planning
, Pittsburgh, PA, Aug. 10–11, pp.
87
93
.
37.
Milani
,
A. A.
, and
Hamedi
,
M.
,
2008
, “
A Knowledge-Based System for Selecting Fastening Tools in Automobile Assembly Lines
,”
IEEE International Conference on Industrial Technology
(
ICIT
), Chengdu, China, Apr. 21–24, pp.
1
7
.
38.
Chung
,
C.
, and
Peng
,
Q.
,
2009
, “
Tool Selection-Embedded Optimal Assembly Planning in a Dynamic Manufacturing Environment
,”
Comput. Des.
,
41
(
7
), pp.
501
512
.
39.
Gottschalk
,
S.
,
2000
, “
Collision Queries Using Oriented Bounding Box
,”
Ph.D. dissertation
,
University of North Carolina
,
Chapel Hill, NC
.https://dl.acm.org/citation.cfm?id=932845
40.
Rosenblatt
,
F.
,
1958
, “
The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain
,”
Psychol. Rev.
,
65
(
6
), pp.
386
408
.
41.
Sassano
,
M.
,
2008
, “
An Experimental Comparison of the Voted Perceptron and Support Vector Machines in Japanese Analysis Tasks
,”
International Joint Conference on Natural Language Processing
, Hyderabad, India, Jan. 7–12, pp. 829–834.http://www.aclweb.org/anthology/I08-2117
42.
Park
,
S.
,
Savvides
,
M.
,
Vasilescu
,
M.
,
Terzopoulos
,
D.
,
Vasilescu
,
M.
,
Terzopoulos
,
D.
,
Fukunaga
,
K.
,
Martinez
,
A.
,
Kak
,
A.
,
Turk
,
M.
,
Pentland
,
A.
,
Yan
,
S.
,
Xu
,
D.
,
Yang
,
Q.
,
Zhang
,
L.
,
Tang
,
X.
,
Zhang
,
H.-J.
,
Golub
,
G.
,
Loan
,
C.
,
De Lathauwer
,
L.
,
De Moor
,
B.
,
Vandewalle
,
J.
,
Li
,
Y.
,
Du
,
Y.
,
Lin
,
X.
,
Scholkopf
,
B.
,
Smola
,
A.
,
Muller
,
K.-R.
,
Wang
,
X.
,
Tang
,
X.
,
Yu
,
H.
,
Yang
,
J.
,
Baudat
,
G.
,
Anouar
,
F.
,
Mika
,
S.
,
Ratsch
,
G.
,
Weston
,
J.
,
Scholkopf
,
B.
,
Muller
,
K.-R.
,
Lee
,
K.-C.
,
Ho
,
J.
,
Kriegman
,
D.
,
Sim
,
T.
,
Baker
,
S.
, and
Bsat
,
M.
,
2010
, “
A Multifactor Extension of Linear Discriminant Analysis for Face Recognition Under Varying Pose and Illumination
,”
EURASIP J. Adv. Signal Process.
,
2010
, p. 158395.
43.
Torkkola
,
K.
,
2004
, “
Discriminative Features for Text Document Classification
,”
Form. Pattern Anal. Appl.
,
6
(
4
), pp.
301
308
.
44.
Gibbs
,
M.
, and
MacKay
,
D. J. C.
,
1997
, “Efficient Implementation of Gaussian Processes,” Cavendish Laboratory, Cambridge, UK,
Technical Report
.http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=A940735F3468921320AF94CFFD4A6B28?doi=10.1.1.48.8224&rep=rep1&type=pdf
45.
McMaster-Carr, 2016, “McMaster-Carr,” McMaster-Carr, Elmhurst, IL, accessed Nov. 16, 2017, http://www.mcmaster.com/
46.
ESI Group,
2016
, “IC.IDO Virtual Reality Software,” Computer Software, ESI-Group, Paris, France.
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