Abstract

Sensors play an important role in monitoring manufacturing processes and update their digital twins. However, the data transmission bandwidth and sensor placement limitations in the physical systems may not allow us to collect the amount or the type of data that we wish. Recently, a physics-based compressive sensing (PBCS) approach was proposed to monitor manufacturing processes and obtain high-fidelity information with the reduced number of sensors by incorporating physical models of processes in compressed sensing. It can recover and reconstruct complete three-dimensional temperature distributions based on some limited measurements. In this paper, a constrained orthogonal matching pursuit algorithm is developed for PBCS, where coherence exists between the measurement matrix and the basis matrix. The efficiency of recovery is improved by introducing a boundary-domain reduction approach, which reduces the size of PBCS model matrices during the inverse operations. The improved PBCS method is demonstrated with the measurement of temperature distributions in the cooling and real-time printing processes of fused filament fabrication.

Issue Section:
Special Issue: Digital Twin Driven Design and Manufacturing
Keywords:
process monitoring, digital twin, compressed sensing, physics-based compressive sensing, PDE constrained optimization, computational foundations for additive manufacturing, inverse methods for engineering applications, process modeling for engineering applications
Topics:
Additive manufacturing, Cooling, Physics, Temperature, Temperature distribution, Errors, Process monitoring, Gears

References

1.
Tao
,
F.
,
Zhang
,
M.
, and
Nee
,
A. Y. C.
,
2019
,
Digital Twin Driven Smart Manufacturing
,
Academic Press
,
London
.
2.
Barring
,
M.
,
Shao
,
G.
, and
Johansson
,
B.
,
2020
, “
Digital Twin for Smart Manufacturing: The Practitioner’s Perspective
,”
Proceedings of the 2020 ASME International Mechanical Engineering Congress and Exposition
,
Virtual Conference
,
Nov. 16–19
, Vol.
84492
, p.
V02BT02A015
.
3.
Rezaei Aderiani
,
A.
,
Wärmefjord
,
K.
,
Söderberg
,
R.
, and
Lindkvist
,
L.
,
2019
, “
Individualizing Locator Adjustments of Assembly Fixtures Using a Digital Twin
,”
ASME J. Comput. Inf. Sci. Eng.
,
19
(
4
), p.
041019
. 10.1115/1.4043529
4.
Shevtshenko
,
E.
,
Mahmood
,
K.
,
Karaulova
,
T.
, and
Raji
,
I. O.
,
2020
, “
Multitier Digital Twin Approach for Agile Supply Chain Management
,”
Proceedings of the 2020 ASME International Mechanical Engineering Congress and Exposition
,
Virtual Conference
,
Nov. 16–19
, Vol.
84492
, p.
V02BT02A012
.
5.
Cai
,
Y.
,
Starly
,
B.
,
Cohen
,
P.
, and
Lee
,
Y. S.
,
2017
, “
Sensor Data and Information Fusion to Construct Digital-Twins Virtual Machine Tools for Cyber-Physical Manufacturing
,”
Procedia Manuf.
,
10
, pp.
1031
–
1042
. 10.1016/j.promfg.2017.07.094
6.
Zhuang
,
C.
,
Liu
,
J.
, and
Xiong
,
H.
,
2018
, “
Digital Twin-Based Smart Production Management and Control Framework for the Complex Product Assembly Shop-Floor
,”
Int. J. Adv. Manuf. Technol.
,
96
(
1–4
), pp.
1149
–
1163
. 10.1007/s00170-018-1617-6
7.
Boschert
,
S.
, and
Rosen
,
R.
,
2016
, “Digital Twin—The Simulation Aspect,”
Mechatronic Futures
,
Springer
,
Cham
, pp.
59
–
74
.
8.
Aivaliotis
,
P.
,
Georgoulias
,
K.
, and
Chryssolouris
,
G.
,
2019
, “
The Use of Digital Twin for Predictive Maintenance in Manufacturing
,”
Int. J. Computer Integrated Manuf.
,
32
(
11
), pp.
1067
–
1080
. 10.1080/0951192X.2019.1686173
9.
Tao
,
F.
,
Cheng
,
J.
,
Qi
,
Q.
,
Zhang
,
M.
,
Zhang
,
H.
, and
Sui
,
F.
,
2018
, “
Digital Twin-Driven Product Design, Manufacturing and Service With Big Data
,”
Int. J. Adv. Manuf. Technol.
,
94
(
9–12
), pp.
3563
–
3576
. 10.1007/s00170-017-0233-1
10.
Uhlemann
,
T. H. J.
,
Schock
,
C.
,
Lehmann
,
C.
,
Freiberger
,
S.
, and
Steinhilper
,
R.
,
2017
, “
The Digital Twin: Demonstrating the Potential of Real Time Data Acquisition in Production Systems
,”
Procedia Manuf.
,
9
, pp.
113
–
120
. 10.1016/j.promfg.2017.04.043
11.
Avventuroso
,
G.
,
Silvestri
,
M.
, and
Pedrazzoli
,
P.
,
2017
, “
A Networked Production System to Implement Virtual Enterprise and Product Lifecycle Information Loops
,”
IFAC-PapersOnLine
,
50
(
1
), pp.
7964
–
7969
. 10.1016/j.ifacol.2017.08.902
12.
Candes
,
E. J.
, and
Tao
,
T.
,
2006
, “
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
,”
IEEE Transactions Information Theory
,
52
(
12
), pp.
5406
–
5425
. 10.1109/TIT.2006.885507
13.
Donoho
,
D. L.
,
2006
, “
Compressed Sensing
,”
IEEE Transactions Information Theory
,
52
(
4
), pp.
1289
–
1306
. 10.1109/TIT.2006.871582
14.
Sun
,
Y.
,
Sun
,
S.
,
Zhang
,
J.
, and
Liu
,
S.
,
2016
, “
Reconstruction of Wind Velocity Distribution Using POD Model
,”
Energy Procedia
,
100
, pp.
137
–
140
. 10.1016/j.egypro.2016.10.155
15.
Bai
,
Z.
,
Wimalajeewa
,
T.
,
Berger
,
Z.
,
Wang
,
G.
,
Glauser
,
M.
, and
Varshney
,
P. K.
,
2015
, “
Low-Dimensional Approach for Reconstruction of Airfoil Data via Compressive Sensing
,”
AIAA J.
,
53
(
4
), pp.
920
–
933
. 10.2514/1.J053287
16.
Lu
,
Y.
, and
Wang
,
Y.
,
2018
, “
Monitoring Temperature in Additive Manufacturing With Physics-Based Compressive Sensing
,”
J. Manuf. Syst.
,
48
, pp.
60
–
70
. 10.1016/j.jmsy.2018.05.010
17.
Lu
,
Y.
, and
Wang
,
Y.
,
2018
, “
An Efficient Transient Temperature Monitoring of Fused Filament Fabrication Process With Physics Based Compressive Sensing
,”
IISE Transactions
,
51
(
2
), pp.
168
–
180
.
18.
Lu
,
Y.
, and
Wang
,
Y.
,
2020
, “
A Physics Based Compressive Sensing Approach to Monitor Turbulent Flow
,”
AIAA J.
,
58
(
8
), pp.
3299
–
3307
. 10.2514/1.J058990
19.
Lu
,
Y.
, and
Wang
,
Y.
,
2019
, “
An Improvement of Physics Based Compressive Sensing With Domain Decomposition to Monitor Temperature in Fused Filament Fabrication Process
,”
ASME 14th International Manufacturing Science and Engineering Conference
,
Erie, PA
,
June 10–14
, Vol.
58745
, p.
V001T01A032
.
20.
Wegner
,
A.
, and
Witt
,
G.
,
2011
, “
Process Monitoring in Laser Sintering Using Thermal Imaging
,”
SFF Symposium
,
Austin, TX
,
Aug. 8–10
, pp.
8
–
10
.
21.
Rodriguez
,
E.
,
Mireles
,
J.
,
Terrazas
,
C. A.
,
Espalin
,
D.
,
Perez
,
M. A.
, and
Wicker
,
R. B.
,
2015
, “
Approximation of Absolute Surface Temperature Measurements of Powder Bed Fusion Additive Manufacturing Technology Using In Situ Infrared Thermography
,”
Addit. Manuf.
,
5
, pp.
31
–
39
. 10.1016/j.addma.2014.12.001
22.
Lane
,
B.
,
Moylan
,
S.
,
Whitenton
,
E. P.
, and
Ma
,
L.
,
2016
, “
Thermographic Measurements of the Commercial Laser Powder Bed Fusion Process at NIST
,”
Rapid Prototyping J.
,
22
(
5
), pp.
778
–
787
. 10.1108/RPJ-11-2015-0161
23.
Bayle
,
F.
, and
Doubenskaia
,
M.
,
2007
, “
Selective Laser Melting Process Monitoring With High Speed Infra-red Camera and Pyrometer
,”
Fundamentals of Laser Assisted Micro-and Nanotechnologies
,
Petersburg, Russia
,
June 25–28
, Vol.
6985
, p.
698505
.
24.
Krauss
,
H.
,
Eschey
,
C.
, and
Zaeh
,
M.
,
2012
, “
Thermography for Monitoring the Selective Laser Melting Process
,”
Proceedings of the Solid Freeform Fabrication Symposium
,
Austin, TX
,
Aug. 6–8
, pp.
999
–
1014
.
25.
Hu
,
D.
,
Mei
,
H.
, and
Kovacevic
,
R.
,
2002
, “
Improving Solid Freeform Fabrication by Laser-Based Additive Manufacturing
,”
Proc. Inst. Mech. Eng. B
,
216
(
9
), pp.
1253
–
1264
. 10.1243/095440502760291808
26.
Hu
,
D.
, and
Kovacevic
,
R.
,
2003
, “
Modelling and Measuring the Thermal Behaviour of the Molten Pool in Closed-Loop Controlled Laser-Based Additive Manufacturing
,”
Proc. Inst. Mech. Eng. B
,
217
(
4
), pp.
441
–
452
. 10.1243/095440503321628125
27.
Dinwiddie
,
R. B.
,
Dehoff
,
R. R.
,
Lloyd
,
P. D.
,
Lowe
,
L. E.
, and
Ulrich
,
J. B.
,
2013
, “
Thermographic In-Situ Process Monitoring of the Electron-Beam Melting Technology Used in Additive Manufacturing
,”
Thermosense: Thermal Infrared Applications XXXV
,
Baltimore, MD
,
Apr. 29–May 3
, Vol.
8705
, p.
87050K
.
28.
Lott
,
P.
,
Schleifenbaum
,
H.
,
Meiners
,
W.
,
Wissenbach
,
K.
,
Hinke
,
C.
, and
Bültmann
,
J.
,
2011
, “
Design of an Optical System for the In situ Process Monitoring of Selective Laser Melting (SLM)
,”
Phys. Procedia
,
12
, pp.
683
–
690
. 10.1016/j.phpro.2011.03.085
29.
Doubenskaia
,
M.
,
Grigoriev
,
S.
,
Zhirnov
,
I.
, and
Smurov
,
I.
,
2016
, “
Parametric Analysis of SLM Using Comprehensive Optical Monitoring
,”
Rapid Prototyping J.
,
22
(
1
), pp.
40
–
50
. 10.1108/RPJ-04-2014-0046
30.
Kleszczynski
,
S.
,
Zur Jacobsmühlen
,
J.
,
Sehrt
,
J. T.
, and
Witt
,
G.
,
2012
, “
Error Detection in Laser Beam Melting Systems by High Resolution Imaging
,”
Proceedings of 23rd Annual International Solid Freeform Fabrication Symposium
,
Austin, TX
,
Aug. 6–8
, pp.
975
–
987
.
31.
Grasso
,
M.
,
Laguzza
,
V.
,
Semeraro
,
Q.
, and
Colosimo
,
B. M.
,
2017
, “
In-process Monitoring of Selective Laser Melting: Spatial Detection of Defects via Image Data Analysis
,”
ASME J. Manuf. Sci. Eng.
,
139
(
5
), p.
051001
. 10.1115/1.4034715
32.
Land
,
W. S.
,
Zhang
,
B.
,
Ziegert
,
J.
, and
Davies
,
A.
,
2015
, “
In-situ Metrology System for Laser Powder Bed Fusion Additive Process
,”
Procedia Manuf.
,
1
, pp.
393
–
403
. 10.1016/j.promfg.2015.09.047
33.
Zhang
,
B.
,
Ziegert
,
J.
,
Farahi
,
F.
, and
Davies
,
A.
,
2016
, “
In Situ Surface Topography of Laser Powder Bed Fusion Using Fringe Projection
,”
Addit. Manuf.
,
12
, pp.
100
–
107
.
34.
Guo
,
Q.
,
Zhao
,
C.
,
Qu
,
M.
,
Xiong
,
L.
,
Hojjatzadeh
,
S. M. H.
,
Escano
,
L. I.
,
Parab
,
N. D.
,
Fezzaa
,
K.
,
Sun
,
T.
, and
Chen
,
L.
,
2020
, “
In-situ Full-Field Mapping of Melt Flow Dynamics in Laser Metal Additive Manufacturing
,”
Addit. Manuf.
,
31
, p.
100939
.
35.
Guo
,
Q.
,
Zhao
,
C.
,
Qu
,
M.
,
Xiong
,
L.
,
Escano
,
L. I.
,
Hojjatzadeh
,
S. M. H.
,
Parab
,
N. D.
,
Fezzaa
,
K.
,
Everhart
,
W.
,
Sun
,
T.
, and
Chen
,
L.
,
2019
, “
In-situ Characterization and Quantification of Melt Pool Variation Under Constant Input Energy Density in Laser Powder Bed Fusion Additive Manufacturing Process
,”
Addit. Manuf.
,
28
, pp.
600
–
609
.
36.
Rieder
,
H.
,
Dillhöfer
,
A.
,
Spies
,
M.
,
Bamberg
,
J.
, and
Hess
,
T.
,
2014
, “
Online Monitoring of Additive Manufacturing Processes Using Ultrasound
,”
Proceedings of the 11th European Conference on Non-Destructive Testing
,
Prague
,
Oct. 6–11
, pp.
6
–
10
.
37.
Wasmer
,
K.
,
Kenel
,
C.
,
Leinenbach
,
C.
, and
Shevchik
,
S. A.
,
2017
, “
In Situ and Real-Time Monitoring of Powder-Bed AM by Combining Acoustic Emission and Artificial Intelligence
,”
International Conference on Additive Manufacturing in Products and Applications
,
Zurich, Switzerland
,
Sept. 13–15
, pp.
200
–
209
.
38.
Dinwiddie
,
R. B.
,
Love
,
L. J.
, and
Rowe
,
J. C.
,
2013
, “
Real-Time Process Monitoring and Temperature Mapping of a 3D Polymer Printing Process
,”
Thermosense: Thermal Infrared Applications XXXV
,
Baltimore, MD
,
Apr. 29–May 3
, Vol.
8705
, p.
87050L
.
39.
Nuchitprasitchai
,
S.
,
Roggemann
,
M.
, and
Pearce
,
J. M.
,
2017
, “
Factors Effecting Real-Time Optical Monitoring of Fused Filament 3D Printing
,”
Progress Additive Manuf.
,
2
(
3
), pp.
133
–
149
.
40.
Wu
,
H.
,
Yu
,
Z.
, and
Wang
,
Y.
,
2016a
, “
A New Approach for Online Monitoring of Additive Manufacturing Based on Acoustic Emission
,”
ASME 2016 11th International Manufacturing Science and Engineering Conference
,
Blacksburg, VA
,
June 27–July 1
, pp.
V003T08A013
–
V003T08A013
.
41.
Wu
,
H.
,
Wang
,
Y.
, and
Yu
,
Z.
,
2016b
, “
In situ Monitoring of FDM Machine Condition via Acoustic Emission
,”
Int. J. Adv. Manuf. Technol.
,
84
(
5–8
), pp.
1483
–
1495
.
42.
Wu
,
H.
,
Yu
,
Z.
, and
Wang
,
Y.
,
2017
, “
Real-Time FDM Machine Condition Monitoring and Diagnosis Based on Acoustic Emission and Hidden Semi-Markov Model
,”
Int. J. Adv. Manuf. Technol.
,
90
(
5–8
), pp.
2027
–
2036
. 10.1007/s00170-016-9548-6
43.
Liu
,
J.
,
Hu
,
Y.
,
Wu
,
B.
, and
Wang
,
Y.
,
2018
, “
An Improved Fault Diagnosis Approach for FDM Process With Acoustic Emission
,”
J. Manuf. Processes
,
35
, pp.
570
–
579
. 10.1016/j.jmapro.2018.08.038
44.
Wu
,
H.
,
Yu
,
Z.
, and
Wang
,
Y.
,
2019
, “
Experimental Study of the Process Failure Diagnosis in Additive Manufacturing Based on Acoustic Emission
,”
Measurement
,
136
, pp.
445
–
453
. 10.1016/j.measurement.2018.12.067
45.
Ceruti
,
A.
,
Liverani
,
A.
, and
Bombardi
,
T.
,
2017
, “
Augmented Vision and Interactive Monitoring in 3D Printing Process
,”
Int. J. Interactive Des. Manuf.
,
11
(
2
), pp.
385
–
395
. 10.1007/s12008-016-0347-y
46.
Cummings
,
I. T.
,
Bax
,
M. E.
,
Fuller
,
I. J.
,
Wachtor
,
A. J.
, and
Bernardin
,
J. D.
,
2017
, “
A Framework for Additive Manufacturing Process Monitoring & Control
,”
Topics in Modal Analysis & Testing
,
Garden Grove, CA
,
Jan. 30–Feb. 2
, Vol.
10
, pp.
137
–
146
.
47.
Kousiatza
,
C.
, and
Karalekas
,
D.
,
2016
, “
In-situ Monitoring of Strain and Temperature Distributions During Fused Deposition Modeling Process
,”
Mater. Des.
,
97
, pp.
400
–
406
. 10.1016/j.matdes.2016.02.099
48.
Faes
,
M.
,
Abbeloos
,
W.
,
Vogeler
,
F.
,
Valkenaers
,
H.
,
Coppens
,
K.
,
Goedemé
,
T.
, and
Ferraris
,
E.
,
2014
, “
Process Monitoring of Extrusion Based 3D Printing via Laser Scanning
,”
PMI 2014 Conference Proceedings
,
Guimaraes, Portugal
,
Sept. 10–12
, Vol.
6
, pp.
363
–
367
.
49.
DebRoy
,
T.
,
Zhang
,
W.
,
Turner
,
J.
, and
Babu
,
S. S.
,
2017
, “
Building Digital Twins of 3D Printing Machines
,”
Scr. Mater.
,
135
, pp.
119
–
124
. 10.1016/j.scriptamat.2016.12.005
50.
Knapp
,
G. L.
,
Mukherjee
,
T.
,
Zuback
,
J. S.
,
Wei
,
H. L.
,
Palmer
,
T. A.
,
De
,
A.
, and
DebRoy
,
T.
,
2017
, “
Building Blocks for a Digital Twin of Additive Manufacturing
,”
Acta Mater.
,
135
, pp.
390
–
399
. 10.1016/j.actamat.2017.06.039
51.
Liu
,
C.
,
Roux
,
L. L.
,
Körner
,
C.
,
Tabaste
,
O.
,
Lacan
,
F.
, and
Bigot
,
S.
,
in press
, “
Digital Twin-Enabled Collaborative Data Management for Metal Additive Manufacturing Systems
,”
J. Manuf. Syst.
52.
Cai
,
Y.
,
Wang
,
Y.
, and
Burnetta
,
M.
,
2020
, “
Using Augmented Reality to Build Digital Twin for Reconfigurable Additive Manufacturing System
,”
J. Manuf. Syst.
,
56
, pp.
598
–
604
.
53.
Mandolla
,
C.
,
Petruzzelli
,
A. M.
,
Percoco
,
G.
,
Urbinati
,
A.
,
Dai
,
W.
, and
Milenkovic
,
O.
,
2019
, “
Building a Digital Twin for Additive Manufacturing Through the Exploitation of Blockchain: A Case Analysis of the Aircraft Industry
,”
Computers Ind.
,
109
, pp.
134
–
152
. 10.1016/j.compind.2019.04.011
54.
Ivanov
,
D.
,
Dolgui
,
A.
,
Das
,
A.
, and
Sokolov
,
B.
,
2019
, “
Digital Supply Chain Twins: Managing the Ripple Effect, Resilience and Disruption Risks by Data-Driven Optimization, Simulation, and Visibility
,”
Handbook of Ripple Effects in the Supply Chain
,
Springer, New York
, pp.
309
–
332
.
55.
Müller
,
J.
,
Panarotto
,
M.
,
Malmqvist
,
J.
, and
Isaksson
,
O.
,
2017
, “
Lifecycle Design and Management of Additive Manufacturing Technologies
,”
Procedia Manuf.
,
19
, pp.
135
–
142
. 10.1016/j.promfg.2018.01.019
56.
Baraniuk
,
R. G.
,
2007
, “
Compressive Sensing
,”
IEEE Signal Processing Magazine
,
24
(
4
), pp.
118
–
121
. 10.1109/MSP.2007.4286571
57.
Eldar
,
Y. C.
, and
Kutyniok
,
G.
,
2012
,
Compressed Sensing: Theory and Applications
,
Cambridge University Press
,
Cambridge
.
58.
Lustig
,
M.
,
Donoho
,
D.
, and
Pauly
,
J. M.
,
2007
, “
Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging
,”
Magnetic Resonance Medicine
,
58
(
6
), pp.
1182
–
1195
. 10.1002/mrm.21391
59.
Duarte
,
M. F.
,
Davenport
,
M. A.
,
Takhar
,
D.
,
Laska
,
J. N.
,
Sun
,
T.
,
Kelly
,
K. E.
, and
Baraniuk
,
R. G.
,
2008
, “
Single-Pixel Imaging via Compressive Sampling
,”
IEEE Signal Processing Magazine
,
25
(
2
), p.
83
. 10.1109/MSP.2007.914730
60.
Haupt
,
J.
,
Bajwa
,
W. U.
,
Rabbat
,
M.
, and
Nowak
,
R.
,
2008
, “
Compressed Sensing for Networked Data
,”
IEEE Signal Processing Magazine
,
25
(
2
), pp.
92
–
101
. 10.1109/MSP.2007.914732
61.
Gan
,
L.
,
2007
, “
Block Compressed Sensing of Natural Images
,”
Proceedings of 2007 IEEE 15th International Conference on Digital Signal Processing
,
Cardiff, UK
,
July 1–4
, pp.
403
–
406
.
62.
FEniCS
.
Software package
,
2011
, https://fenicsproject.org/
Copyright © 2021 by ASME
You do not currently have access to this content.