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Abstract

Robotic additive manufacturing (RAM) offers significant improvements in maximum build volume compared to conventional bounded designs (e.g., gantry) by leveraging high degrees-of-freedom machines and multi-robot cooperation. However, cooperative RAM suffers from the same defect generation challenges as conventional systems, necessitating improvements in the detection and prevention of flaws within fabricated components. Quality assurance can be further bolstered through the integration of AM models, which utilize sensor feedback to localize defects, vastly reducing false positives. This research explores defect localization through a novel dynamic defect model created from simulated sensing data. In particular, two cooperative robots are simulated to estimate defect parameters, while observing the workspace and accurately classifying different regions of the part, generating a Gaussian mixture map that identifies and assigns appropriate actions based on defect types and characteristics. The experimental result shows that the implementation of the dynamic defect model and selective reevaluation achieved an effective defect detection accuracy of 99.9%, an improvement of 9.9% without localization. The proposed framework holds potential for application in domains that utilize high degrees-of-freedom machines and collaborative agents, offering scalability, improved fabrication speeds, and enhanced mechanical properties.

References

1.
Sepasgozar
,
S. M. E.
,
Shi
,
A.
,
Yang
,
L.
,
Shirowzhan
,
S.
, and
Edwards
,
D. J.
,
2020
, “
Additive Manufacturing Applications for Industry 4.0: A Systematic Critical Review
,”
Buildings
,
10
(
12
), p.
231
.
2.
Abdulhameed
,
O.
,
Al-Ahmari
,
A.
,
Ameen
,
W.
, and
Mian
,
S. H.
,
2019
, “
Additive Manufacturing: Challenges, Trends, and Applications
,”
Adv. Mech. Eng.
,
11
(
2
), p.
1687814018822880
.
3.
Vafadar
,
A.
,
Guzzomi
,
F.
,
Rassau
,
A.
, and
Hayward
,
K.
,
2021
, “
Advances in Metal Additive Manufacturing: A Review of Common Processes, Industrial Applications, and Current Challenges
,”
Appl. Sci.
,
11
(
3
), p.
1213
.
4.
Holshouser
,
C.
,
Newell
,
C.
,
Palas
,
S.
,
Love
,
L. J.
,
Kunc
,
V.
,
Lind
,
R. F.
,
Lloyd
,
P. D.
,
Rowe
,
J. C.
,
Blue
,
C. A.
, and
Duty
,
C. E.
,
2013
, “
Out of Bounds Additive Manufacturing
,”
Adv. Mater. Process.
,
171
(
3
), pp.
15
17
.
5.
Duty
,
C. E.
,
Kunc
,
V.
,
Compton
,
B.
,
Post
,
B.
,
Erdman
,
D.
,
Smith
,
R.
,
Lind
,
R.
,
Lloyd
,
P.
, and
Love
,
L.
,
2017
, “
Structure and Mechanical Behavior of Big Area Additive Manufacturing (BAAM) Materials
,”
Rapid Prototyp. J.
,
23
(
1
), pp.
181
189
.
6.
Ali
,
M. H.
,
Kurokawa
,
S.
,
Shehab
,
E.
, and
Mukhtarkhanov
,
M.
,
2023
, “
Development of a Large-Scale Multi-Extrusion FDM Printer, and Its Challenges
,”
Int. J. Lightweight Mater. Manuf.
,
6
(
2
), pp.
198
213
.
7.
Pires
,
J. N.
,
Azar
,
A. S.
,
Nogueira
,
F.
,
Zhu
,
C. Y.
,
Branco
,
R.
, and
Tankova
,
T.
,
2022
, “
The Role of Robotics in Additive Manufacturing: Review of the AM Processes and Introduction of an Intelligent System
,”
Ind. Rob.: Int. J. Rob. Res. Appl.
,
49
(
2
), pp.
311
331
.
8.
Coupek
,
D.
,
Friedrich
,
J.
,
Battran
,
D.
, and
Riedel
,
O.
,
2018
, “
Reduction of Support Structures and Building Time by Optimized Path Planning Algorithmsin Multi-axis Additive Manufacturing
,”
Proc. CIRP
,
67
, pp.
221
226
.
9.
Kraljić
,
D.
, and
Kamnik
,
R.
,
2019
, “
Trajectory Planning for Additive Manufacturing With a 6-DOF Industrial Robot
,”
Advances in Service and Industrial Robotics
,
N. A.
Aspragathos
,
P. N.
Koustoumpardis
, and
V. C.
Moulianitis
, eds.,
Mechanisms and Machine Science, Springer International Publishing
, pp.
456
465
.
10.
Zhang
,
X.
,
Li
,
M.
,
Lim
,
J. H.
,
Weng
,
Y.
,
Tay
,
Y. W. D.
,
Pham
,
H.
, and
Pham
,
Q.-C.
,
2018
, “
Large-Scale 3D Printing by a Team of Mobile Robots
,”
Autom. Constr.
,
95
, pp.
98
106
.
11.
Urhal
,
P.
,
Weightman
,
A.
,
Diver
,
C.
, and
Bartolo
,
P.
,
2019
, “
Robot Assisted Additive Manufacturing: A Review
,”
Rob. Comput.-Integr. Manuf.
,
59
, pp.
335
345
.
12.
Bhatt
,
P. M.
,
Malhan
,
R. K.
,
Shembekar
,
A. V.
,
Yoon
,
Y. J.
, and
Gupta
,
S. K.
,
2020
, “
Expanding Capabilities of Additive Manufacturing Through Use of Robotics Technologies: A Survey
,”
Addit. Manuf.
,
31
, p.
100933
.
13.
Dörfler
,
K.
,
Dielemans
,
G.
,
Lachmayer
,
L.
,
Recker
,
T.
,
Raatz
,
A.
,
Lowke
,
D.
, and
Gerke
,
M.
,
2022
, “
Additive Manufacturing Using Mobile Robots: Opportunities and Challenges for Building Construction’
,”
Cem. Concr. Res.
,
158
, p.
106772
.
14.
Brennan
,
M.
,
Keist
,
J.
, and
Palmer
,
T.
,
2021
, “Defects in Metal Additive Manufacturing Processes”.
15.
Taheri
,
H.
,
Shoaib
,
M. R. B. M.
,
Koester
,
L. W.
,
Bigelow
,
T. A.
,
Collins
,
P. C.
, and
Bond
,
L. J.
,
2017
, “
Powder-Based Additive Manufacturing–A Review of Types of Defects, Generation Mechanisms, Detection, Property Evaluation and Metrology
,”
Int. J. Addit. Subtract. Mater. Manuf.
,
1
(
2
), pp.
172
209
.
16.
Chen
,
Y.
,
Peng
,
X.
,
Kong
,
L.
,
Dong
,
G.
,
Remani
,
A.
, and
Leach
,
R.
,
2021
, “
Defect Inspection Technologies for Additive Manufacturing
,”
Int. J. Extreme Manuf.
,
3
(
2
), p.
022002
.
17.
Du Plessis
,
A.
,
Yadroitsava
,
I.
, and
Yadroitsev
,
I.
,
2020
, “
Effects of Defects on Mechanical Properties in Metal Additive Manufacturing: A Review Focusing on X-Ray Tomography Insights
,”
Mater. Des.
,
187
, p.
108385
.
18.
Jalalahmadi
,
B.
,
Liu
,
J.
,
Rios
,
J.
,
Slotwinski
,
J.
,
Peitsch
,
C.
,
Goldberg
,
A.
, and
Montalbano
,
T.
,
2019
, “
In-Process Defect Monitoring and Correction in Additive Manufacturing of Aluminum Alloys
,”
Vertical Flight Society’s 75th Annual Forum & Technology Display
,
Philadelphia, PA
,
May 13–16
.
19.
Mostafaei
,
A.
,
Zhao
,
C.
,
He
,
Y.
,
Ghiaasiaan
,
S. R.
,
Shi
,
B.
,
Shao
,
S.
,
Shamsaei
,
N.
,
Wu
,
Z.
,
Kouraytem
,
N.
,
Sun
,
T.
, and
Pauza
,
J.
,
2022
, “
Defects and Anomalies in Powder Bed Fusion Metal Additive Manufacturing
,”
Curr. Opin. Solid State Mater. Sci.
,
26
(
2
), p.
100974
.
20.
Honarvar
,
F.
, and
Varvani-Farahani
,
A.
,
2020
, “
A Review of Ultrasonic Testing Applications in Additive Manufacturing: Defect Evaluation, Material Characterization, and Process Control
,”
Ultrasonics
,
108
, p.
106227
.
21.
Meneghetti
,
G.
,
Rigon
,
D.
, and
Gennari
,
C.
,
2019
, “
An Analysis of Defects Influence on Axial Fatigue Strength of Maraging Steel Specimens Produced by Additive Manufacturing
,”
Int. J. Fatigue
,
118
, pp.
54
64
.
22.
Shaloo
,
M.
,
Schnall
,
M.
,
Klein
,
T.
,
Huber
,
N.
, and
Reitinger
,
B.
,
2022
, “
A Review of Non-Destructive Testing (NDT) Techniques for Defect Detection: Application to Fusion Welding and Future Wire Arc Additive Manufacturing Processes
,”
Materials
,
15
(
10
), p.
3697
.
23.
Feng
,
W.
,
Mao
,
Z.
,
Yang
,
Y.
,
Ma
,
H.
,
Zhao
,
K.
,
Qi
,
C.
,
Hao
,
C.
,
Liu
,
Z.
,
Xie
,
H.
, and
Liu
,
S.
,
2022
, “
Online Defect Detection Method and System Based on Similarity of the Temperature Field in the Melt Pool
,”
Addit. Manuf.
,
54
, p.
102760
.
24.
Bowoto
,
O. K.
,
Oladapo
,
B. I.
,
Zahedi
,
S.
,
Omigbodun
,
F. T.
, and
Emenuvwe
,
O. P.
,
2020
, “
Analytical Modelling of In Situ Layer-Wise Defect Detection in 3D-Printed Parts: Additive Manufacturing
,”
Int. J. Adv. Manuf. Technol.
,
111
, pp.
2311
2321
.
25.
Remani
,
A.
,
Williams
,
R.
,
Thompson
,
A.
,
Dardis
,
J.
,
Jones
,
N.
,
Hooper
,
P.
, and
Leach
,
R.
,
2021
, “
Design of a Multi-Sensor Measurement System for In Situ Defect Identification in Metal Additive Manufacturing
,” Proceedings of Euspen Advancing Precision in Additive Manufacturing.
26.
Colosimo
,
B. M.
,
Grossi
,
E.
,
Caltanissetta
,
F.
, and
Grasso
,
M.
,
2020
, “
Penelope: A Novel Prototype for In Situ Defect Removal in LPBF
,”
JOM
,
72
(
3
), pp.
1332
1339
.
27.
Borish
,
M.
,
Post
,
B. K.
,
Roschli
,
A.
,
Chesser
,
P. C.
, and
Love
,
L. J.
,
2020
, “
Real-Time Defect Correction in Large-Scale Polymer Additive Manufacturing Via Thermal Imaging and Laser Profilometer
,”
Proc. Manuf.
,
48
, pp.
625
633
.
28.
Gökhan Demir
,
A.
,
De Giorgi
,
C.
, and
Previtali
,
B.
,
2018
, “
Design and Implementation of a Multisensor Coaxial Monitoring System With Correction Strategies for Selective Laser Melting of a Maraging Steel
,”
ASME J. Manuf. Sci. Eng.
,
140
(
4
), p.
041003
.
29.
Yang
,
Z.
,
Lu
,
Y.
,
Yeung
,
H.
, and
Krishnamurty
,
S.
,
2020
, “
From Scan Strategy to Melt Pool Prediction: A Neighboring-Effect Modeling Method
,”
ASME J. Comput. Inf. Sci. Eng.
,
20
(
5
), p.
051001
.
30.
Moges
,
T.
,
Yang
,
Z.
,
Jones
,
K.
,
Feng
,
S.
,
Witherell
,
P.
, and
Lu
,
Y.
,
2021
, “
Hybrid Modeling Approach for Melt-Pool Prediction in Laser Powder Bed Fusion Additive Manufacturing
,”
ASME J. Comput. Inf. Sci. Eng.
,
21
(
5
), p.
050902
.
31.
Akhil
,
V.
,
Raghav
,
G.
,
Arunachalam
,
N.
, and
Srinivas
,
D.
,
2020
, “
Image Data-Based Surface Texture Characterization and Prediction Using Machine Learning Approaches for Additive Manufacturing
,”
ASME J. Comput. Inf. Sci. Eng.
,
20
(
2
), p.
021010
.
32.
Poudel
,
L.
,
Blair
,
C.
,
McPherson
,
J.
,
Sha
,
Z.
, and
Zhou
,
W.
,
2020
, “
A Heuristic Scaling Strategy for Multi-robot Cooperative Three-Dimensional Printing
,”
ASME J. Comput. Inf. Sci. Eng.
,
20
(
4
), p.
041002
.
33.
Poudel
,
L.
,
Zhou
,
W.
, and
Sha
,
Z.
,
2020
, “
A Generative Approach for Scheduling Multi-robot Cooperative Three-Dimensional Printing
,”
ASME J. Comput. Inf. Sci. Eng.
,
20
(
6
), p.
061011
.
34.
Zimermann
,
R.
,
Mohseni
,
E.
,
Vasilev
,
M.
,
Loukas
,
C.
,
Vithanage
,
R. K.
,
Macleod
,
C. N.
,
Lines
,
D.
,
Javadi
,
Y.
,
Espirindio E Silva
,
M. P.
,
Fitzpatrick
,
S.
, and
Halavage
,
S.
,
2022
, “
Collaborative Robotic Wire+ Arc Additive Manufacture and Sensor-Enabled In-Process Ultrasonic Non-Destructive Evaluation
,”
Sensors
,
22
(
11
), p.
4203
.
35.
Jin
,
Z.
,
Zhang
,
Z.
,
Ott
,
J.
, and
Gu
,
G. X.
,
2021
, “
Precise Localization and Semantic Segmentation Detection of Printing Conditions in Fused Filament Fabrication Technologies Using Machine Learning
,”
Addit. Manuf.
,
37
, p.
101696
.
36.
Qin
,
J.
,
Hu
,
F.
,
Liu
,
Y.
,
Witherell
,
P.
,
Wang
,
C. C.
,
Rosen
,
D. W.
,
Simpson
,
T.
,
Lu
,
Y.
, and
Tang
,
Q.
,
2022
, “
Research and Application of Machine Learning for Additive Manufacturing
,”
Addit. Manuf.
,
52
, p.
102691
.
37.
Dharmawan
,
A. G.
,
Xiong
,
Y.
,
Foong
,
S.
, and
Soh
,
G. S.
,
2020
, “
A Model-Based Reinforcement Learning and Correction Framework for Process Control of Robotic Wire Arc Additive Manufacturing
,”
IEEE International Conference on Robotics and Automation (ICRA)
,
Paris, France
,
May 31–Aug. 31
, IEEE, pp.
4030
4036
.
38.
Xu
,
P.
,
Yao
,
X.
,
Chen
,
L.
,
Zhao
,
C.
,
Liu
,
K.
,
Moon
,
S. K.
, and
Bi
,
G.
,
2022
, “
In-Process Adaptive Dimension Correction Strategy for Laser Aided Additive Manufacturing Using Laser Line Scanning
,”
J. Mater. Process. Technol.
,
303
, p.
117544
.
39.
Bonaccorso
,
F.
,
Cantelli
,
L.
, and
Muscato
,
G.
,
2011
, “
An Arc Welding Robot Control for a Shaped Metal Deposition Plant: Modular Software Interface and Sensors
,”
IEEE Trans. Ind. Electron.
,
58
(
8
), pp.
3126
3132
.
40.
Jiang
,
J.
,
Newman
,
S. T.
, and
Zhong
,
R. Y.
,
2021
, “
A Review of Multiple Degrees of Freedom for Additive Manufacturing Machines
,”
Int. J. Comput. Integr. Manuf.
,
34
(
2
), pp.
195
211
.
41.
Ding
,
D.
,
Pan
,
Z. S.
,
Cuiuri
,
D.
, and
Li
,
H.
,
2014
, “
A Tool-Path Generation Strategy for Wire and Arc Additive Manufacturing
,”
Int. J. Adv. Manuf. Technol.
,
73
(
1
), pp.
173
183
.
42.
Ding
,
D.
,
Pan
,
Z.
,
Cuiuri
,
D.
, and
Li
,
H.
,
2015
, “
A Multi-bead Overlapping Model for Robotic Wire and Arc Additive Manufacturing (WAAM)
,”
Rob. Comput.-Integr. Manuf.
,
31
, pp.
101
110
.
43.
Ding
,
D.
,
Pan
,
Z.
,
Cuiuri
,
D.
, and
Li
,
H.
,
2015
, “
A Practical Path Planning Methodology for Wire and Arc Additive Manufacturing of Thin-Walled Structures
,”
Rob. Comput.-Integr. Manuf.
,
34
, pp.
8
19
.
44.
Zhang
,
G. Q.
,
Mondesir
,
W.
,
Martinez
,
C.
,
Li
,
X.
,
Fuhlbrigge
,
T. A.
, and
Bheda
,
H.
,
2015
, “
Robotic Additive Manufacturing Along Curved Surface – A Step Towards Free-Form Fabrication
,”
IEEE International Conference on Robotics and Biomimetics (ROBIO)
,
Zhuhai, China
,
Dec. 6–9
, pp.
721
726
.
45.
Ding
,
D.
,
Pan
,
Z.
,
Cuiuri
,
D.
,
Li
,
H.
, and
Larkin
,
N.
,
2016
, “
Adaptive Path Planning for Wire-Feed Additive Manufacturing Using Medial Axis Transformation
,”
J. Clean. Prod.
,
133
, pp.
942
952
.
46.
Ding
,
D.
,
Shen
,
C.
,
Pan
,
Z.
,
Cuiuri
,
D.
,
Li
,
H.
,
Larkin
,
N.
, and
van Duin
,
S.
,
2016
, “
Towards an Automated Robotic Arc-Welding-Based Additive Manufacturing System From CAD to Finished Part
,”
Comput.-Aid. Des.
,
73
, pp.
66
75
.
47.
Doherty
,
S.
,
De Backer
,
W.
,
Bergs
,
A. P.
,
Harik
,
R.
,
van Tooren
,
M.
, and
Rekleitis
,
I.
,
2016
, “
Selective Directional Reinforcement of Structures for Multi-axis Additive Manufacturing
,”
CAMX Conference Proceedings
,
Anaheim, CA
,
Sept. 26–29
.
48.
Ding
,
Y.
, and
Kovacevic
,
R.
,
2016
, “
Feasibility Study on 3-D Printing of Metallic Structural Materials With Robotized Laser-Based Metal Additive Manufacturing
,”
JOM
,
68
(
7
), pp.
1774
1779
.
49.
Felsch
,
T.
,
Klaeger
,
U.
,
Steuer
,
J.
,
Schmidt
,
L.
, and
Schilling
,
M.
,
2017
, “
Robotic System for Additive Manufacturing of Large and Complex Parts
,”
22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
,
Limassol, Cyprus
,
Sept. 12–15
, pp.
1
4
.
50.
Danielsen Evjemo
,
L.
,
Moe
,
S.
,
Gravdahl
,
J. T.
,
Roulet-Dubonnet
,
O.
,
Gellein
,
L. T.
, and
Brtan
,
V.
,
2017
, “
Additive Manufacturing by Robot Manipulator: An Overview of the State-of-the-Art and Proof-of-Concept Results
,”
22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
,
Limassol, Cyprus
,
Sept. 12–15
, pp.
1
8
.
51.
Tiryaki
,
M. E.
,
Zhang
,
X.
, and
Pham
,
Q. -C.
,
2019
, “
Printing-While-Moving: A New Paradigm for Large-Scale Robotic 3D Printing
,”
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
,
Macau, China
,
Nov. 3–8
, pp.
2286
2291
.
52.
Shen
,
H.
,
Sun
,
W.
, and
Fu
,
J.
,
2019
, “
Multi-view Online Vision Detection Based on Robot Fused Deposit Modeling 3D Printing Technology
,”
Rapid. Prototyp. J.
,
25
(
2
), pp.
343
355
.
53.
Shembekar
,
A. V.
,
Yoon
,
Y. J.
,
Kanyuck
,
A.
, and
Gupta
,
S. K.
,
2019
, “
Generating Robot Trajectories for Conformal Three-Dimensional Printing Using Nonplanar Layers
,”
ASME J. Comput. Inf. Sci. Eng.
,
19
(
3
), p.
031011
.
54.
Li
,
Y.
,
Xiong
,
J.
, and
Yin
,
Z.
,
2019
, “
Molten Pool Stability of Thin-Wall Parts in Robotic GMA-Based Additive Manufacturing with Various Position Depositions
,”
Rob. Comput.-Integr. Manuf.
,
56
, pp.
1
11
.
55.
Ishak
,
I. B.
, and
Larochelle
,
P.
,
2019
, “
MotoMaker: a Robot FDM Platform for Multi-plane and 3D Lattice Structure Printing
,”
Mech. Based Des. Struct. Mach.
,
47
(
6
), pp.
703
720
.
56.
Alhijaily
,
A.
,
Kilic
,
Z. M.
, and
Bartolo
,
A. N. P.
,
2023
, “
Teams of Robots in Additive Manufacturing: a Review
,”
Virtual Phys. Prototyp.
,
18
(
1
), p.
e2162929
.
57.
Shen
,
H.
,
Pan
,
L.
, and
Qian
,
J.
,
2019
, “
Research on Large-Scale Additive Manufacturing Based on Multi-robot Collaboration Technology
,”
Addit. Manuf.
,
30
, p.
100906
.
58.
Xu
,
X.
,
Wang
,
Z.
, and
Feng
,
C.
,
2021
, Projector-Guided Non-Holonomic Mobile 3D Printing, May. arXiv:2105.08950.
59.
Meng
,
L.
,
McWilliams
,
B.
,
Jarosinski
,
W.
,
Park
,
H.-Y.
,
Jung
,
Y.-G.
,
Lee
,
J.
, and
Zhang
,
J.
,
2020
, “
Machine Learning in Additive Manufacturing: A Review
,”
JOM
,
72
(
6
), pp.
2363
2377
.
60.
Wang
,
C.
,
Tan
,
X.
,
Tor
,
S.
, and
Lim
,
C.
,
2020
, “
Machine Learning in Additive Manufacturing: State-of-the-Art and Perspectives
,”
Addit. Manuf.
,
36
, p.
101538
.
61.
Qin
,
J.
,
Hu
,
F.
,
Liu
,
Y.
,
Witherell
,
P.
,
Wang
,
C. C. L.
,
Rosen
,
D. W.
,
Simpson
,
T. W.
,
Lu
,
Y.
, and
Tang
,
Q.
,
2022
, “
Research and Application of Machine Learning for Additive Manufacturing
,”
Addit. Manuf.
,
52
, p.
102691
.
62.
Chen
,
L.
,
Yao
,
X.
,
Feng
,
W.
,
Chew
,
Y.
, and
Moon
,
S. K.
,
2023
, “Multimodal Sensor Fusion for Real-Time Location-Dependent Defect Detection in Laser-Directed Energy Deposition,” arXiv preprint arXiv:2305.13596.
63.
Yaseer
,
A.
, and
Chen
,
H.
,
2021
, “
Machine Learning Based Layer Roughness Modeling in Robotic Additive Manufacturing
,”
J. Manuf. Process.
,
70
, pp.
543
552
.
64.
Snow
,
Z.
,
Diehl
,
B.
,
Reutzel
,
E. W.
, and
Nassar
,
A.
,
2021
, “
Toward In-Situ Flaw Detection in Laser Powder Bed Fusion Additive Manufacturing Through Layerwise Imagery and Machine Learning
,”
J. Manuf. Syst.
,
59
, pp.
12
26
.
65.
Westphal
,
E.
, and
Seitz
,
H.
,
2021
, “
A Machine Learning Method for Defect Detection and Visualization in Selective Laser Sintering Based on Convolutional Neural Networks
,”
Addit. Manuf.
,
41
, p.
101965
.
66.
Ertay
,
D. S.
,
Kamyab
,
S.
,
Vlasea
,
M.
,
Azimifar
,
Z.
,
Ma
,
T.
,
Rogalsky
,
A. D.
, and
Fieguth
,
P.
,
2021
, “
Toward Sub-Surface Pore Prediction Capabilities for Laser Powder Bed Fusion Using Data Science
,”
ASME J. Manuf. Sci. Eng.
,
143
(
7
), p.
071016
.
67.
Davtalab
,
O.
,
Kazemian
,
A.
,
Yuan
,
X.
, and
Khoshnevis
,
B.
,
2022
, “
Automated Inspection in Robotic Additive Manufacturing Using Deep Learning for Layer Deformation Detection
,”
J. Intell. Manuf.
,
33
(
3
), pp.
771
784
.
68.
Li
,
W.
,
Zhang
,
H.
,
Wang
,
G.
,
Xiong
,
G.
,
Zhao
,
M.
,
Li
,
G.
, and
Li
,
R.
,
2023
, “
Deep Learning Based Online Metallic Surface Defect Detection Method for Wire and Arc Additive Manufacturing
,”
Rob. Comput.-Integr. Manuf.
,
80
, p.
102470
.
69.
Siciliano
,
B.
,
1990
, “
A Closed-Loop Inverse Kinematic Scheme for On-Line Joint-Based Robot Control
,”
Robotica
,
8
(
3
), pp.
231
243
.
70.
Yoshikawa
,
T.
,
1985
, “
Manipulability of Robotic Mechanisms
,”
Int. J. Rob. Res.
,
4
(
2
), pp.
3
9
.
71.
Wolcott
,
R. W.
, and
Eustice
,
R. M.
,
2015
, “
Fast Lidar Localization Using Multiresolution Gaussian Mixture Maps
,”
IEEE International Conference on Robotics and Automation (ICRA)
,
Seattle, WA
,
May 26–30
, IEEE, pp.
2814
2821
.
72.
Martínez
,
A.
,
Díez
,
J.
,
Verde
,
P.
,
Ferrero
,
R.
,
Álvarez
,
R.
,
Perez
,
H.
, and
Vizán
,
A.
,
2021
, “
Digital Twin for the Integration of the Automatic Transport and Manufacturing Processes
,” In IOP Conference Series: Materials Science and Engineering, Vol.
1193
,
IOP Publishing
, p.
012107
.
73.
Kam
,
H. R.
,
Lee
,
S.-H.
,
Park
,
T.
, and
Kim
,
C.-H.
,
2015
, “
Rviz: A Toolkit for Real Domain Data Visualization
,”
Telecommun. Syst.
,
60
, pp.
337
345
.
74.
Wang
,
Y.
,
Huang
,
J.
,
Wang
,
Y.
,
Feng
,
S.
,
Peng
,
T.
,
Yang
,
H.
, and
Zou
,
J.
,
2020
, “
A CNN-Based Adaptive Surface Monitoring System for Fused Deposition Modeling
,”
IEEE/ASME Trans. Mechatron.
,
25
(
5
), pp.
2287
2296
.
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