TY - JOUR
T1 - Assisted defect detection by in-process monitoring of additive manufacturing using optical imaging and infrared thermography
AU - AbouelNour, Youssef
AU - Gupta, Nikhil
N1 - Funding Information:
The authors would like to thank Hammond Pearce for his assistance in developing an algorithm for intentional defect formation. Funding for this work is supported by the 2022 American Society of Non-destructive Testing ( ASNT ) Fellowship Award and the National Science Foundation grant CMMI-2036802 . The views expressed in this article are those of the authors and not of the funding agencies.
Publisher Copyright:
© 2023 The Authors
PY - 2023/4/5
Y1 - 2023/4/5
N2 - Layer-wise in-process monitoring in Fused Filament Fabrication (FFF) 3D printing can facilitate the detection of defects introduced during manufacturing. In this work, optical imaging and infrared (IR) thermography were used simultaneously for the detection of embedded defects, such as point and line defects. The optical images helped in identifying the necessary variables that can lead to real-time defect detection through image correlation. Through temperature monitoring and thermal image analysis, defect detection was accomplished by comparing to a baseline. It was found that as the number of embedded defects increased in a specimen, the average specimen temperature, T̃specimen, increased. An increase in the number of defects by 2X and 5X led to an increase in T̃specimen that is ∼18X and 37X the relative standard error. There was also a positive correlation between the global average hotspot temperature, T̃hotspot, and the total number of embedded defects in the specimen. This study demonstrates that in-situ defect detection can be accomplished using optical and thermal imaging systems.
AB - Layer-wise in-process monitoring in Fused Filament Fabrication (FFF) 3D printing can facilitate the detection of defects introduced during manufacturing. In this work, optical imaging and infrared (IR) thermography were used simultaneously for the detection of embedded defects, such as point and line defects. The optical images helped in identifying the necessary variables that can lead to real-time defect detection through image correlation. Through temperature monitoring and thermal image analysis, defect detection was accomplished by comparing to a baseline. It was found that as the number of embedded defects increased in a specimen, the average specimen temperature, T̃specimen, increased. An increase in the number of defects by 2X and 5X led to an increase in T̃specimen that is ∼18X and 37X the relative standard error. There was also a positive correlation between the global average hotspot temperature, T̃hotspot, and the total number of embedded defects in the specimen. This study demonstrates that in-situ defect detection can be accomplished using optical and thermal imaging systems.
KW - Additive manufacturing
KW - Defect detection
KW - In-process monitoring
KW - Optical imaging
KW - Thermal imaging
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U2 - 10.1016/j.addma.2023.103483
DO - 10.1016/j.addma.2023.103483
M3 - Article
AN - SCOPUS:85150073797
SN - 2214-8604
VL - 67
JO - Additive Manufacturing
JF - Additive Manufacturing
M1 - 103483
ER -