While the combination of 3D scanning and printing processes holds much promise for the field of new product development, problems with repeatability and accuracy have limited the wider spread adoption of some digital prototyping tools, such as 3D scanners. Studies have explored the errors inherent in higher fidelity scan to print (S2P) processes, yet few have explored the errors in S2P processes that leverage affordable rapid noncontact scanners. Studies have yet to explore the strategies that designers, who are experienced with additive manufacturing, employ to mitigate errors. To address these gaps, a controlled study was conducted using data from 27 scans collected with a prototypical off-the-shelf noncontact optical scanner. The geometric and dimensional integrity of the digital models was found to be significantly out of tolerance at various phases of the S2P process, as compared to the original physical model. Larger errors were found more consistently in the data acquisition phase of the S2P process, but results indicate these errors were not sufficiently filtered out during the remainder of the process. A behavioral study was conducted with 13 experienced designers in digital fabrication to determine strategies for manually cleaning Point Clouds. Actions such as increase or decrease in brush size and select or de-select points were recorded. These actions were analyzed using hidden Markov modeling, which revealed distinct patterns of behavior. Designer strategies were not beneficial and digital models produced by designers were found to be significantly smaller than original physical models.

References

References
1.
Rayna
,
T.
, and
Striukova
,
L.
,
2016
, “
From Rapid Prototyping to Home Fabrication: How 3D Printing Is Changing Business Model Innovation
,”
Technol. Forecast. Soc. Change
,
102
, pp.
214
224
.
2.
Otto
,
K. N.
, and
Wood
,
K. L.
,
1998
, “
Product Evolution: A Reverse Engineering and Redesign Methodology
,”
Res. Eng. Des.
,
10
(
4
), pp.
226
243
.
3.
Bagaria
,
V.
,
Deshpande
,
S.
,
Rasalkar
,
D. D.
,
Kuthe
,
A.
, and
Paunipagar
,
B. K.
,
2011
, “
Use of Rapid Prototyping and Three-Dimensional Reconstruction Modeling in the Management of Complex Fractures
,”
Eur. J. Radiol.
,
80
(
3
), pp.
814
820
.
4.
Galantucci, L. M.
,
Piperi, E.
,
Lavecchia, F.
, and
Zhavo, A.
, 2015, “
Semi-Automatic Low Cost 3D Laser Scanning Systems for Reverse Engineering
,”
Procedia CIRP
,
28
, pp. 94–99.
5.
Sun
,
W.
,
Starly
,
B.
,
Nam
,
J.
, and
Darling
,
A.
,
2005
, “
Bio-CAD Modeling and Its Applications in Computer-Aided Tissue Engineering
,”
CAD Comput. Aided Des.
,
37
(
11
), pp.
1097
1114
.
6.
Starly
,
B.
,
Fang
,
Z.
,
Sun
,
W.
,
Shokoufandeh
,
A.
, and
Regli
,
W.
,
2005
, “
Three-Dimensional Reconstruction for Medical-CAD Modeling
,”
Comput. Aided. Des. Appl.
,
2
(
1–4
), pp.
431
438
.
7.
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.
,
26
(
11–12
), pp.
1284
1288
.
8.
Kietzmann
,
J.
,
Pitt
,
L.
, and
Berthon
,
P.
,
2015
, “
Disruptions, Decisions, and Destinations: Enter the Age of 3-D Printing and Additive Manufacturing
,”
Bus. Horiz.
,
58
(
2
), pp.
209
215
.
9.
Li
,
L.
,
Schemenauer
,
N.
,
Peng
,
X.
,
Zeng
,
Y.
, and
Gu
,
P.
,
2002
, “
A Reverse Engineering System for Rapid Manufacturing of Complex Objects
,”
Robot. Comput. Integr. Manuf.
,
18
(
1
), pp.
53
67
.
10.
Piller
,
F. T.
,
2007
, “
Observations on the Present and Future of Mass Customization
,”
Int. J. Flex. Manuf. Syst.
,
19
(
4
), pp.
630
636
.
11.
Anwer
,
N.
, and
Mathieu
,
L.
,
2016
, “
From Reverse Engineering to Shape Engineering in Mechanical Design
,”
CIRP Ann. Manuf. Technol.
,
65
(
1
), pp.
165
168
.
12.
Várady
,
T.
,
Martin
,
R. R.
, and
Cox
,
J.
,
1997
, “
Reverse Engineering of Geometric Models—An Introduction
,”
Comput. Des.
,
29
(
4
), pp.
255
268
.
13.
Huotilainen
,
E.
,
Jaanimets
,
R.
,
Valášek
,
J.
,
Marcián
,
P.
,
Salmi
,
M.
,
Tuomi
,
J.
,
Mäkitie
,
A.
, and
Wolff
,
J.
,
2014
, “
Inaccuracies in Additive Manufactured Medical Skull Models Caused by the DICOM to STL Conversion Process
,”
J. Cranio-Maxillofac. Surg.
,
42
(
5
), pp. e259–e265.
14.
Van Eijnatten
,
M.
,
Rijkhorst
,
E. J.
,
Hofman
,
M.
,
Forouzanfar
,
T.
, and
Wolff
,
J.
,
2016
, “
The Accuracy of Ultrashort Echo Time MRI Sequences for Medical Additive Manufacturing
,”
Dentomaxillofac. Radiol.
,
45
(
5
), pp. 1–8.
15.
Salmi
,
M.
,
Paloheimo
,
K. S.
,
Tuomi
,
J.
,
Wolff
,
J.
, and
Mäkitie
,
A.
,
2013
, “
Accuracy of Medical Models Made by Additive Manufacturing (Rapid Manufacturing)
,”
J. Cranio-Maxillofac. Surg.
,
41
(
7
), pp.
603
609
.
16.
Sansoni
,
G.
, and
Docchio
,
F.
,
2004
, “
Three-Dimensional Optical Measurements and Reverse Engineering for Automotive Applications
,”
Robot. Comput. Integr. Manuf.
,
20
(
5
), pp.
359
367
.
17.
Sadler
,
J.
,
Shluzas
,
L.
,
Blikstein
,
P.
, and
Katila
,
R.
,
2015
, “
Building Blocks of the Maker Movement: Modularity Enhances Creative Confidence During Prototyping
,”
Design Thinking Research: Making Design Thinking Foundational
, Springer, Cham, Switzerland, pp.
141
154
.
18.
Chivate
,
P. N.
, and
Jablokow
,
A. G.
,
1993
, “
Solid-Model Generation From Measured Point Data
,”
Comput. Des.
,
25
(
9
), pp.
587
600
.
19.
Cantisani
,
G.
,
Nuikka
,
M.
,
Rönnholm
,
P.
,
Kaartinen
,
H.
,
Kukku
,
A.
,
Suominen
,
A.
,
Salo
,
P.
,
Pötinen
,
P.
,
Hyyppä
,
H.
,
Hyyppä
,
J.
,
Haggrén
,
H.
,
Absetz
,
I.
,
Puttonen
,
J.
,
Hirsi
,
H.
,
Tang
,
P.
,
Huber
,
D.
,
Akinci
,
B.
, and
Boukamp
,
F.
,
2015
, “
Characterization of Laser Scanners and Algorithms for Detecting Flatness Defects on Concrete Surfaces
,”
J. Comput. Civ. Eng.
,
4
(
1
), pp.
129
134
.
20.
Berger
,
M.
,
Tagliasacchi
,
A.
,
Seversky
,
L. M.
,
Alliez
,
P.
,
Guennebaud
,
G.
,
Levine
,
J. A.
,
Sharf
,
A.
, and
Silva
,
C. T.
,
2017
, “
A Survey of Surface Reconstruction From Point Clouds
,”
Comput. Graph. Forum
,
36
(
1
), pp.
301
329
.
21.
Le
,
T.
,
Chartrand
,
R.
, and
Asaki
,
T. J.
,
2007
, “
A Variational Approach to Reconstructing Images Corrupted by Poisson Noise
,”
J. Math. Imaging Vis.
,
27
(
3
), pp.
257
263
.
22.
Szilvśi-Nagy
,
M.
, and
Mátyási
,
G.
,
2003
, “
Analysis of STL Files
,”
Math. Comput. Model.
,
38
(
7–9
), pp.
945
960
.
23.
Sharma
,
N.
, and
Aggarwal
,
L. M.
,
2010
, “
Automated Medical Image Segmentation Techniques
,”
J. Med. Phys.
,
35
(
1
), pp.
3
14
.
24.
Reyes
,
A.
,
Turkyilmaz
,
I.
, and
Prihoda
,
T. J.
,
2015
, “
Accuracy of Surgical Guides Made From Conventional and a Combination of Digital Scanning and Rapid Prototyping Techniques
,”
J. Prosthet. Dent.
,
113
(
4
), pp.
295
303
.
25.
Pham
,
D. L.
,
Xu
,
C.
, and
Prince
,
J. L.
,
2000
, “
Current Methods in Medical Image Segmentation
,”
Annu. Rev. Biomed. Eng.
,
2
, pp.
315
337
.
26.
Segreto
,
T.
,
Caggiano
,
A.
, and
D'Addona
,
D. M.
,
2013
, “
Assessment of Laser-Based Reverse Engineering Systems for Tangible Cultural Heritage Conservation
,”
Int. J. Comput. Integr. Manuf.
,
26
(
9
), pp.
857
865
.
27.
Berger
,
M.
,
Alliez
,
P.
,
Tagliasacchi
,
A.
,
Seversky
,
L. M.
,
Silva
,
C. T.
,
Levine
,
J. A.
, and
Sharf
,
A.
,
2014
, “
State of the Art in Surface Reconstruction From Point Clouds
,”
Eurographics Conferences
, Strasbourg, France, Apr. 7–11, pp.
161
185
.
28.
Kazhdan
,
M.
, and
Hoppe
,
H.
,
2013
, “
Screened Poisson Surface Reconstruction
,”
ACM Trans. Graphics
,
32
(
3
), pp.
1
13
.
29.
Yin
,
Z. W.
,
2011
, “
Direct Generation of Extended STL File From Unorganized Point Data
,”
CAD Comput. Aided Des
,
43
(
6
), pp.
699
706
.
30.
Hiller
,
J. D.
, and
Lipson
,
H.
,
2009
, “
STL 2.0: A Proposal for a Universal Multi-Material Additive Manufacturing File Format
,”
20th Solid Freeform Fabrication Symposium
, Austin, TX, Aug. 12–14, pp.
266
278
.
31.
Rypl
,
D.
, and
Bittnar
,
Z.
,
2006
, “
Generation of Computational Surface Meshes of STL Models
,”
J. Comput. Appl. Math.
,
192
(
1
), pp.
148
151
.
32.
Lee
,
S. H.
,
Kim
,
H. C.
,
Hur
,
S. M.
, and
Yang
,
D. Y.
,
2002
, “
STL File Generation From Measured Point Data by Segmentation and Delaunay Triangulation
,”
CAD Comput. Aided Des.
,
34
(
10
), pp.
691
704
.
33.
Lanzotti
,
A.
,
Del Giudice
,
D. M.
,
Lepore
,
A.
,
Staiano
,
G.
, and
Martorelli
,
M.
,
2015
, “
On the Geometric Accuracy of RepRap Open-Source Three-Dimensional Printer
,”
ASME J. Mech. Des
,
137
(
10
), p.
101703
.
34.
Farzadi
,
A.
,
Solati-Hashjin
,
M.
,
Asadi-Eydivand
,
M.
, and
Osman
,
N. A. A.
,
2014
, “
Effect of Layer Thickness and Printing Orientation on Mechanical Properties and Dimensional Accuracy of 3D Printed Porous Samples for Bone Tissue Engineering
,”
PLoS One
,
9
(
9
), p. e108252.
35.
Dimitrov
,
D.
,
van Wijck
,
W.
,
Schreve
,
K.
, and
de Beer
,
N.
,
2006
, “
Investigating the Achievable Accuracy of Three Dimensional Printing
,”
Rapid Prototyp. J.
,
12
(
1
), pp.
42
52
.
36.
Lieneke
,
T.
,
Adam
,
G. A. O.
,
Leuders
,
S.
,
Knoop
,
F.
,
Josupeit
,
S.
,
Delfs
,
P.
,
Funke
,
N.
, and
Zimmer
,
D.
,
2015
, “
Systematical Determination of Tolerances for Additive Manufacturing by Measuring Linear Dimensions
,”
Solid Freeform Fabrication Symposium—An Additive Manufacturing Conference
, Austin, TX, Aug. 10–15, pp.
371
384
.
37.
Lieneke
,
T.
,
Denzer
,
V.
,
Adam
,
G. A. O.
, and
Zimmer
,
D.
,
2016
, “
Dimensional Tolerances for Additive Manufacturing: Experimental Investigation for Fused Deposition Modeling
,”
Procedia CIRP
,
43
, pp.
286
291
.
38.
Manmadhachary
,
A. Y.
,
Ravi Kumar
,
Y.
, and
Krishnanand
,
L.
,
2016
, “
Improve the Accuracy, Surface Smoothing and Material Adaption in STL File for RP Medical Models
,”
J. Manuf. Process.
,
21
, pp.
46
55
.
39.
Jablokow
,
K.
,
Teerlink
,
W.
,
Yilmaz
,
S.
,
Daly
,
S.
,
Silk
,
E.
, and
Wehr
,
C.
,
2015
, “
Ideation Variety in Mechanical Design: Examining the Effects of Cognitive Style and Design Heuristics
,”
ASME
Paper No. DETC2015-46334.
40.
Daly
,
S.
, and
Christian
,
J. L.
,
2012
, “
Assessing Design Heuristics for Idea Generation in an Introductory Engineering Course
,”
Int. J. Eng. Educ.
,
28
(
2
), pp.
463
473
.https://lib.dr.iastate.edu/cgi/viewcontent.cgi?referer=https://www.google.co.in/&httpsredir=1&article=1003&context=industrialdesign_pubs
41.
Daly
,
S. R.
,
Yilmaz
,
S.
,
Christian
,
J. L.
,
Seifert
,
C. M.
, and
Gonzalez
,
R.
,
2012
, “
Design Heuristics in Engineering Concept Generation
,”
J. Eng. Educ.
,
101
(
4
), pp.
601
629
.
42.
Kramer
,
J.
,
Daly
,
S. R.
,
Yilmaz
,
S.
,
Seifert
,
C. M.
, and
Gonzalez
,
R.
,
2015
, “
Investigating the Impacts of Design Heuristics on Idea Initiation and Development
,”
Adv. Eng. Educ.
,
4
(
4
), p. 25.
43.
Yilmaz
,
S.
,
Daly
,
S. R.
,
Seifert
,
C. M.
, and
Gonzalez
,
R.
,
2016
, “
Evidence-Based Design Heuristics for Idea Generation
,”
Des. Stud.
,
46
, pp.
95
124
.
44.
McComb
,
C.
,
Cagan
,
J.
, and
Kotovsky
,
K.
,
2018
, “
Eliciting Configuration Design Heuristics With Hidden Markov Models
,”
International Conference on Engineering Design (ICED)
, Vancouver, BC, Canada, Aug. 21–25.
45.
McComb
,
C.
,
Cagan
,
J.
, and
Kotovsky
,
K.
,
2017
, “
Capturing Human Sequence-Learning Abilities in Configuration Design Tasks Through Markov Chains
,”
ASME J. Mech. Des.
,
139
(9), p. 091101.
46.
McComb
,
C.
,
Cagan
,
J.
, and
Kotovsky
,
K.
,
2015
, “
Rolling With the Punches: An Examination of Team Performance in a Design Task Subject to Drastic Changes
,”
Des. Stud.
,
36
(
1
), pp.
99
121
.
47.
McComb
,
C.
,
Cagan
,
J.
, and
Kotovsky
,
K.
,
2014
, “
Quantitative Comparison of High- And Low-Performing Teams in a Design Task Subject to Drastic Changes
,”
ASME
Paper No. DETC2014-34653.
48.
Yilmaz
,
S.
,
Seifert
,
C. M.
, and
Gonzalez
,
R.
,
2010
, “
Cognitive Heuristics in Design: Instructional Strategies to Increase Creativity in Idea Generation
,”
Artif. Intell. Eng. Des. Anal. Manuf.
,
24
(
3
), pp.
335
355
.
49.
Deininger
,
M.
,
Daly
,
S. R.
,
Sienko
,
K. H.
, and
Lee
,
J. C.
,
2017
, “
Novice Designers' Use of Prototypes in Engineering Design
,”
Des. Stud.
,
51
, pp.
25
65
.
50.
Carrington
,
P.
,
Hosmer
,
S.
,
Yeh
,
T.
,
Hurst
,
A.
, and
Kane
,
S. K.
,
2015
, “
Like This, But Better': Supporting Novices' Design and Fabrication of 3D Models Using Existing Objects
,”
Proc. iConference
, Newport Beach, CA, Mar. 24–27.
51.
Sinha
,
S.
,
Chen
,
H.
,
Meisel
,
N. A.
, and
Miller
,
S. R.
,
2017
, “
Does Designing for Additive Manufacturing Help Us Be More Creative? An Exploration in Engineering Design Education
,”
ASME
Paper No. DETC2017-68274.
52.
Anderson
,
J. R.
,
1982
, “
Acquisition of Cognitive Skill
,”
Psychol. Rev.
,
89
(4), pp. 369–406.
53.
Wright
,
S. M.
,
Silk
,
E. M.
,
Daly
,
S. R.
,
Jablokow
,
K. W.
, and
Yilmaz
,
S.
,
2015
, “
Exploring the Effects of Problem Framing on Solution Shifts: A Case Study
,”
ASEE Annual Conference and Exposition
, Seattle, WA, June 14–17.
54.
McComb
,
C.
,
Cagan
,
J.
, and
Kotovsky
,
K.
,
2017
, “
Utilizing Markov Chains to Understand Operation Sequencing in Design Tasks
,”
Design Computing and Cognition '16
, pp. 401–408.
55.
Shark, D.
, and
Townsend, T.
, 2018. “
Matter and Form Home
,” Matter and Form, Inc., Torronto, ON, Canada, accessed Nov. 23, 2018, https://matterandform.net/
56.
Arvanitis
,
G.
,
Lalos
,
A. S.
,
Moustakas
,
K.
, and
Fakotakis
,
N.
,
2017
, “
Real-Time Removing of Outliers and Noise in 3D Point Clouds Applied in Robotic Applications
,” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing AG, New York, pp.
11
19
.
57.
Kazhdan
,
M.
,
Bolitho
,
M.
, and
Hoppe
,
H.
,
2006
, “
Poisson Surface Reconstruction
,”
Symposium on Geometry Processing
, Sardinia, Italy, June 26–28, pp.
61
70
.
58.
Johnson
,
E.
,
2011
, “
STL File Reader—File Exchange—MATLAB Central
,” MathWorks, Natick, MA.
59.
Römera
,
G. R. B. E.
, and
Huisin't Veld
,
A. J.
,
2010
, “
Matlab Laser Toolbox
,”
Phys. Procedia
,
5
(B), pp.
413
419
.
60.
Palinkas
,
L. A.
,
Horwitz
,
S. M.
,
Green
,
C. A.
,
Wisdom
,
J. P.
,
Duan
,
N.
, and
Hoagwood
,
K.
,
2015
, “
Purposeful Sampling for Qualitative Data Collection and Analysis in Mixed Method Implementation Research
,”
Adm. Policy Ment. Health
,
42
(5), pp. 533–544.
61.
Vogt
,
W. P.
,
Gardner
,
D. C.
, and
Haeffele
,
L. M.
,
2012
,
When to Use What Research Design
, Guilford Press, New York.
62.
Turk
,
G.
, and
Levoy
,
M.
,
1994
, “
Zippered Polygon Meshes From Range Images
,”
21st Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH)
, Orlando, FL, July 24–29.
63.
Loose
,
J. P.
,
Zhou
,
Q.
,
Zhou
,
S.
, and
Ceglarek
,
D.
,
2010
, “
Integrating GD&T Into Dimensional Variation Models for Multistage Machining Processes
,”
Int. J. Prod. Res.
,
48
(
11
), pp.
3129
3149
.
64.
McComb
,
C.
,
Cagan
,
J.
, and
Kotovsky
,
K.
,
2017
, “
Mining Process Heuristics From Designer Action Data Via Hidden Markov Models
,”
ASME J. Mech. Des.
,
139
(
11
), p.
111412
.
65.
Arlot
,
S.
, and
Celisse
,
A.
,
2010
, “
A Survey of Cross-Validation Procedures for Model Selection
,”
Stat. Surv.
,
4
, pp.
40
79
.
66.
Baum
,
L. E.
,
Petrie
,
T.
,
Soules
,
G.
, and
Weiss
,
N.
,
1970
, “
A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
,”
Ann. Math. Stat.
,
41
(
1
), pp.
164
171
.
67.
Moroni
,
G.
,
Syam
,
W. P.
, and
Petró
,
S.
,
2014
, “
Towards Early Estimation of Part Accuracy in Additive Manufacturing
,”
Procedia CIRP
,
21
, pp.
300
305
.
68.
Johnson
,
W. M.
,
Rowell
,
M.
,
Deason
,
B.
, and
Eubanks
,
M.
,
2011
, “
Benchmarking Evaluation of an Open Source Fused Deposition Modeling Additive Manufacturing System
,”
22nd Annual International Solid Freeform Fabrication Symposium
, Austin, TX, Aug. 8–10, pp.
197
211
.
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