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ASTM Selected Technical Papers
Progress in Additive Manufacturing 2021
Editor
Nima Shamsaei
Nima Shamsaei
Symposium Chair and STP Editor
1
Auburn University
,
Auburn, AL,
US
Search for other works by this author on:
Nik Hrabe
Nik Hrabe
Symposium Chair and STP Editor
2
National Institute of Standards and Technology
,
Boulder, CO,
US
Search for other works by this author on:
Mohsen Seifi
Mohsen Seifi
Symposium Chair and STP Editor
3
ASTM International
,
Washington, DC,
US
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ISBN:
978-0-8031-7735-2
No. of Pages:
274
Publisher:
ASTM International
Publication date:
2022

In situ monitoring is an essential technique required to rationalize the use of expensive additive manufacturing (AM) by interrupting and correcting defects when the material is deposited rather than having to detect and correct them by postprocessing after the fact. Implementing in situ monitoring can be difficult for several reasons, all of which are linked to the feasibility of detecting manufacturing issues in real time so that corrective actions can be taken as and when required. Among these essential reasons, one could list the following: form factor, energy consumption, heat dissipation of the in situ equipment within the AM complete installation, the available network connectivity allowing for conventional processing approaches such as cloud computing, and the level of performance required to monitor, analyze, and take appropriate actions (e.g., alerting, interrupting the process) in real time. AIoT is an acronym denominating the convergence between AI techniques and IoT hardware, including sensors, computation, and automation. It refers to a panoply of new-generation devices and solutions that can provide novel approaches for tackling AM in situ monitoring, whether they are implemented entirely on one of several IoT components embedded in the AM equipment or in conjunction with computing power locally adjoined (on premises) to the AIoT capabilities within the AM equipment or even with remote computing power (either edge/fog or cloud computing, thus making the AIoT-based solution a hybrid solution). We consider the feasibility of all three options by illustrating and assessing the feasibility, with an adequate experimental protocol, of providing AIoT solutions capable of supporting the stringent in situ monitoring requirements for AM.

1.
Standard Terminology for Additive Manufacturing—General Principles—Terminology
, ISO/ASTM 52900 (
Geneva, Switzerland
:
International Organization for Standardization
,
2015
).
2.
National Institute of Standards and Technology
,
Measurement Science Roadmap for Metal-Based Additive Manufacturing
(
Columbia, MD
:
Energetics Inc.
,
2013
), https://perma.cc/T2TA-KD7H
3.
Selema
A.
,
Ibrahim
M. N.
, and
Sergeant
P.
, “
Metal Additive Manufacturing for Electrical Machines: Technology Review and Latest Advancements
,”
Energies
15
, no.
3
(
2022
): 1076,
4.
Dilberoglu
U. M.
,
Gharehpapagh
B.
,
Yaman
U.
, and
Dolen
M.
, “
The Role of Additive Manufacturing in the Era of Industry 4.0
,”
Procedia Manufacturing
11
(
2017
): 545–554.
5.
Chen
Z.
,
Han
C.
,
Gao
M.
,
Kandukuri
S. Y.
, and
Zhou
K.
, “
A Review on Qualification and Certification for Metal Additive Manufacturing
,”
Virtual and Physical Prototyping
17
, no.
2
(
2022
): 382–405.
6.
Lehmhus
D.
,
Aumund-Kopp
C.
,
Petzoldt
F.
,
Godlinski
D.
,
Haberkorn
A.
,
Zöllmer
V.
, and
Busse
M.
, “
Customized Smartness: A Survey on Links between Additive Manufacturing and Sensor Integration
,”
Procedia Technology
26
(
2016
): 284–301.
7.
Scime
L.
and
Beuth
J.
, “
Anomaly Detection and Classification in a Laser Powder Bed Additive Manufacturing Process Using a Trained Computer Vision Algorithm
,”
Additive Manufacturing
19
(
2018
): 114–126.
8.
Whitento
E. P.
, High-Speed Dual-Spectrum Imaging for the Measurement of Metal Cutting Temperatures, NIST Interagency/Internal Report 7650 (Gaithersburg, MD:
National Institute of Standards and Technology
,
2010
).
9.
Ayala Meza
R. B.
and
Farret
J.
, “
Profiling the Performance and Energy Efficiency of Edge Accelerators in the Context of Computer Vision
,” in
Advances in Signal Processing and Artificial Intelligence: Proceedings of the 2nd International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2020)
, ed.
Yurish
S. Y.
(
Barcelona, Spain
:
IFSA Publishing, S.L.
,
2020
), 244–249.
10.
Soleymani
R.
and
Farret
J.
, “
Text Classification with Transformers and Reformers for Deep Text Data
,” in
Advances in Signal Processing and Artificial Intelligence: Proceedings of the 2nd International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2020)
, ed.
Yurish
S. Y.
(
Barcelona, Spain
:
IFSA Publishing, S.L.
,
2020
), 239–243.
11.
Baghdadi
N.
and
Farret
J.
, “
Deep Learning at the Edge: Performance Evaluation of Deep Learning Models on the Edge Device
,” in
Advances in Signal Processing and Artificial Intelligence: Proceedings of the 3rd International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2021)
, ed.
Yurish
S. Y.
(
Barcelona, Spain
:
IFSA Publishing, S.L.
,
2021
), 61–65.
12.
Ullah
I.
and
Mahmoud
Q. H.
, “
Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks
,”
IEEE Access
9
(
2021
): 103906–103926.
13.
Molnar
B.
,
Heigel
J. C.
, and
Whitenton
E.
, “
In Situ Thermography during Laser Powder Bed Fusion of a Nickel Superalloy 625 Artifact with Various Overhangs and Supports
,”
Journal of Research of the National Institute of Standards and Technology
126
(
2021
): 126005,
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