testbed. Its distributed computing architecture across three
layers prevented bottlenecks, enabling prompt defect correc-
tion. The framework’s automatic feature extraction and real-
time feedback significantly improved confidence in defect
detection for complex geometries. The main contribution of
this work is the development of a digital thread framework
for heterogeneous data streams to detect damage in real time
and reach a decision-level state. One key development is the
integration of a real-time DEF model with multimodal sensing.
The model itself was unique in integrating two heterogeneous
sensing streams for real-time classification. The model includes
a custom loss function, which improved defect classification
results. The DEF model has been designed to accommodate
flexible data inputs, state-of-the-art model architectures, and
outputs for various applications. This strategy facilitated the
integration of various data types in real time, thereby enrich-
ing the depth and precision of defect detection. Moreover, the
trained model was integrated with a real-time digital thread. Its
contributions lay the foundation for future research, empha-
sizing the need for greater adaptability and the integration of
advanced technologies in manufacturing.
ACKNOWLEDGMENTS
The financial support that Sarah Malik received from the National Science
Foundation (NSF) through the NSF Graduate Research Fellowship
Program and the ASME Donald O. Thompson Graduate Research Fellow-
ship, as well as the support she received from the Graduate Assistance in
Areas of National Need (GAANN) fellowship, is greatly appreciated. Sincere
gratitude is extended to RTX Corp. (formerly Raytheon Technologies) and
MISTRAS Group for financial support and engagement in various parts of
this study. Finally, special thanks to the members of the Theoretical and
Applied Mechanics Group at Drexel University for their support, and espe-
cially to Hadi Khezam, Dhruv Shah, and Jason Voutsinas.
REFERENCES
1. Wu, H., Y. Wang, and Z. Yu. 2016. “In situ monitoring of FDM
machine condition via acoustic emission.” International Journal of
Advanced Manufacturing Technology 84 (5-8): 1483–95.
https://doi.org/10.1007/s00170-015-7809-4
2. Fu, Y., A. Downey, L. Yuan, A. Pratt, and Y. Balogun. 2021. “In situ moni-
toring for fused filament fabrication process: A review.” Additive Manufac-
turing 38: 101749. https://doi.org/10.1016/j.addma.2020.101749
3. Yoon, J., D. He, and B. Van Hecke. 2014. “A PHM approach to
additive manufacturing equipment health monitoring, fault diagnosis,
and quality control.” Annual Conference of the PHM Society 6 (1).
https://doi.org/10.36001/phmconf.2014.v6i1.2338
4. Lu, Q. Y., and C. H. Wong. 2018. “Additive manufacturing process moni-
toring and control by non-destructive testing techniques: challenges and
in-process monitoring.” Virtual and Physical Prototyping 13 (2): 39–48.
https://doi.org/10.1080/17452759.2017.1351201
5. Ramírez, I. S., F. P. G. Márquez, and M. Papaelias. 2023. “Review on
additive manufacturing and non-destructive testing.” Journal of Manufac-
turing Systems 66: 260–86. https://doi.org/10.1016/j.jmsy.2022.12.005
6. Ferraris, E., J. Zhang, and B. Van Hooreweder. 2019. “Thermography
based in-process monitoring of Fused Filament Fabrication of
polymeric parts.” CIRP Annals (68) 1: 213–216.
https://doi.org/10.1016/j.cirp.2019.04.123
7. Tlegenov, Y., W. F. Lu, and G. S. Hong. 2019. “A dynamic model for
current-based nozzle condition monitoring in fused deposition
modelling.” Progress in Additive Manufacturing 4 (3): 211–23.
https://doi.org/10.1007/s40964-019-00089-3
8. Rao, P. K., J. Liu, D. Roberson, Z. Kong, and C. Williams. 2015. “Online
Real-Time Quality Monitoring in Additive Manufacturing Processes Using
Heterogeneous Sensors.” Journal of Manufacturing Science and Engi-
neering 137 (6): 061007. https://doi.org/10.1115/1.4029823
9. Akhavan, J., and S. Manoochehri. 2022. “Sensory data fusion using
machine learning methods for in-situ defect registration in additive
manufacturing: a review.” In 2022 IEEE International IOT, Electronics,
and Mechatronics Conference (IEMTRONICS), pp. 1-10. IEEE.
https://doi.org/10.1109/IEMTRONICS55184.2022.9795815
10. Wu, H., Z. Yu, and Y. Wang. 2019. “Experimental study of
the process failure diagnosis in additive manufacturing based
on acoustic emission.” Measurement 136: 445–53.
https://doi.org/10.1016/j.measurement.2018.12.067
11. Muktadir, M., M. N. Hasan, and M. Alam. 2022. “Additive manufac-
turing and acoustic Emission: A brief review.” Journal of Additive Manufac-
turing Technologies 2 (1): 632. https://doi.org/10.18416/JAMTECH.2212632
12. Wisner, B., K. Mazur, V. Perumal, K. P. Baxevanakis, L. An, G. Feng,
and A. Kontsos. 2019. “Acoustic emission signal processing framework to
identify fracture in aluminum alloys.” Engineering Fracture Mechanics.
210: 367–380. https://doi.org/10.1016/j.engfracmech.2018.04.027
13. Malik, S., and A. Kontsos. 2024. “A novel information entropy
approach for crack monitoring leveraging nondestructive evaluation
sensing.” Mechanical Systems and Signal Processing 214: 111207.
https://doi.org/10.1016/j.ymssp.2024.111207
14. Liu, J., Y. Hu, B. Wu, and Y. Wang. 2018. “An improved fault diagnosis
approach for FDM process with acoustic emission.” Journal of Manufac-
turing Processes 35: 570–79. https://doi.org/10.1016/j.jmapro.2018.08.038
15. Delli, U., and S. Chang. 2018. “Automated process monitoring in 3D
printing using supervised machine learning.” Procedia Manufacturing 26:
865–70. https://doi.org/10.1016/j.promfg.2018.07.111
16. Gaja, H., and F. Liou. 2017. “Defects monitoring of laser metal
deposition using acoustic emission sensor.” International Journal
of Advanced Manufacturing Technology 90 (1-4): 561–74.
https://doi.org/10.1007/s00170-016-9366-x
17. Wu, M., V. V. Phoha, Y. B. Moon, and A. K. Belman. 2016. “Detecting
malicious defects in 3d printing process using machine learning and
image classification.” Proceedings of the ASME 2016 International
Mechanical Engineering Congress and Exposition (14): V014T07A004.
https://doi.org/10.1115/IMECE2016-67641
18. Bacha, A., A. H. Sabry, and J. Benhra. 2019. “Fault Diagnosis in the
Field of Additive Manufacturing (3D Printing) Using Bayesian Networks.”
International Journal of Online &Biomedical Engineering 15 (3): 110–23.
https://doi.org/10.3991/ijoe.v15i03.9375
19. Wang, C., X. Tan, S. Tor, and C. Lim. 2020. “Machine learning in
additive manufacturing: State-of-the-art and perspectives.” Additive
Manufacturing 36: 101538. https://doi.org/10.1016/j.addma.2020.101538
20. Meng, L., B. McWilliams, W. Jarosinski, H.-Y. Park, Y.-G. Jung, J. Lee,
and J. Zhang. 2020. “Machine learning in additive manufacturing:
A review.” Journal of the Minerals Metals &Materials Society 72 (6):
2363–77. https://doi.org/10.1007/s11837-020-04155-y
21. Menon, A., B. Póczos, A. W. Feinberg, and N. R. Washburn. 2019.
“Optimization of silicone 3D printing with hierarchical machine
learning.” 3D Printing and Additive Manufacturing 6 (4): 181–189.
https://doi.org/10.1089/3dp.2018.0088
22. Mohammadi, M. G., D. Mahmoud, and M. Elbestawi. 2021.
“On the application of machine learning for defect detection in L-PBF
additive manufacturing.” Optics &Laser Technology 143: 107338.
https://doi.org/10.1016/j.optlastec.2021.107338
23. Scime, L., and J. Beuth. 2018. “A multi-scale convolutional neural
network for autonomous anomaly detection and classification in a laser
powder bed fusion additive manufacturing process.” Additive Manufac-
turing 24: 273–86. https://doi.org/10.1016/j.addma.2018.09.034
J U LY 2 0 2 6 • M AT E R I A L S E V A L U AT I O N 51
layers prevented bottlenecks, enabling prompt defect correc-
tion. The framework’s automatic feature extraction and real-
time feedback significantly improved confidence in defect
detection for complex geometries. The main contribution of
this work is the development of a digital thread framework
for heterogeneous data streams to detect damage in real time
and reach a decision-level state. One key development is the
integration of a real-time DEF model with multimodal sensing.
The model itself was unique in integrating two heterogeneous
sensing streams for real-time classification. The model includes
a custom loss function, which improved defect classification
results. The DEF model has been designed to accommodate
flexible data inputs, state-of-the-art model architectures, and
outputs for various applications. This strategy facilitated the
integration of various data types in real time, thereby enrich-
ing the depth and precision of defect detection. Moreover, the
trained model was integrated with a real-time digital thread. Its
contributions lay the foundation for future research, empha-
sizing the need for greater adaptability and the integration of
advanced technologies in manufacturing.
ACKNOWLEDGMENTS
The financial support that Sarah Malik received from the National Science
Foundation (NSF) through the NSF Graduate Research Fellowship
Program and the ASME Donald O. Thompson Graduate Research Fellow-
ship, as well as the support she received from the Graduate Assistance in
Areas of National Need (GAANN) fellowship, is greatly appreciated. Sincere
gratitude is extended to RTX Corp. (formerly Raytheon Technologies) and
MISTRAS Group for financial support and engagement in various parts of
this study. Finally, special thanks to the members of the Theoretical and
Applied Mechanics Group at Drexel University for their support, and espe-
cially to Hadi Khezam, Dhruv Shah, and Jason Voutsinas.
REFERENCES
1. Wu, H., Y. Wang, and Z. Yu. 2016. “In situ monitoring of FDM
machine condition via acoustic emission.” International Journal of
Advanced Manufacturing Technology 84 (5-8): 1483–95.
https://doi.org/10.1007/s00170-015-7809-4
2. Fu, Y., A. Downey, L. Yuan, A. Pratt, and Y. Balogun. 2021. “In situ moni-
toring for fused filament fabrication process: A review.” Additive Manufac-
turing 38: 101749. https://doi.org/10.1016/j.addma.2020.101749
3. Yoon, J., D. He, and B. Van Hecke. 2014. “A PHM approach to
additive manufacturing equipment health monitoring, fault diagnosis,
and quality control.” Annual Conference of the PHM Society 6 (1).
https://doi.org/10.36001/phmconf.2014.v6i1.2338
4. Lu, Q. Y., and C. H. Wong. 2018. “Additive manufacturing process moni-
toring and control by non-destructive testing techniques: challenges and
in-process monitoring.” Virtual and Physical Prototyping 13 (2): 39–48.
https://doi.org/10.1080/17452759.2017.1351201
5. Ramírez, I. S., F. P. G. Márquez, and M. Papaelias. 2023. “Review on
additive manufacturing and non-destructive testing.” Journal of Manufac-
turing Systems 66: 260–86. https://doi.org/10.1016/j.jmsy.2022.12.005
6. Ferraris, E., J. Zhang, and B. Van Hooreweder. 2019. “Thermography
based in-process monitoring of Fused Filament Fabrication of
polymeric parts.” CIRP Annals (68) 1: 213–216.
https://doi.org/10.1016/j.cirp.2019.04.123
7. Tlegenov, Y., W. F. Lu, and G. S. Hong. 2019. “A dynamic model for
current-based nozzle condition monitoring in fused deposition
modelling.” Progress in Additive Manufacturing 4 (3): 211–23.
https://doi.org/10.1007/s40964-019-00089-3
8. Rao, P. K., J. Liu, D. Roberson, Z. Kong, and C. Williams. 2015. “Online
Real-Time Quality Monitoring in Additive Manufacturing Processes Using
Heterogeneous Sensors.” Journal of Manufacturing Science and Engi-
neering 137 (6): 061007. https://doi.org/10.1115/1.4029823
9. Akhavan, J., and S. Manoochehri. 2022. “Sensory data fusion using
machine learning methods for in-situ defect registration in additive
manufacturing: a review.” In 2022 IEEE International IOT, Electronics,
and Mechatronics Conference (IEMTRONICS), pp. 1-10. IEEE.
https://doi.org/10.1109/IEMTRONICS55184.2022.9795815
10. Wu, H., Z. Yu, and Y. Wang. 2019. “Experimental study of
the process failure diagnosis in additive manufacturing based
on acoustic emission.” Measurement 136: 445–53.
https://doi.org/10.1016/j.measurement.2018.12.067
11. Muktadir, M., M. N. Hasan, and M. Alam. 2022. “Additive manufac-
turing and acoustic Emission: A brief review.” Journal of Additive Manufac-
turing Technologies 2 (1): 632. https://doi.org/10.18416/JAMTECH.2212632
12. Wisner, B., K. Mazur, V. Perumal, K. P. Baxevanakis, L. An, G. Feng,
and A. Kontsos. 2019. “Acoustic emission signal processing framework to
identify fracture in aluminum alloys.” Engineering Fracture Mechanics.
210: 367–380. https://doi.org/10.1016/j.engfracmech.2018.04.027
13. Malik, S., and A. Kontsos. 2024. “A novel information entropy
approach for crack monitoring leveraging nondestructive evaluation
sensing.” Mechanical Systems and Signal Processing 214: 111207.
https://doi.org/10.1016/j.ymssp.2024.111207
14. Liu, J., Y. Hu, B. Wu, and Y. Wang. 2018. “An improved fault diagnosis
approach for FDM process with acoustic emission.” Journal of Manufac-
turing Processes 35: 570–79. https://doi.org/10.1016/j.jmapro.2018.08.038
15. Delli, U., and S. Chang. 2018. “Automated process monitoring in 3D
printing using supervised machine learning.” Procedia Manufacturing 26:
865–70. https://doi.org/10.1016/j.promfg.2018.07.111
16. Gaja, H., and F. Liou. 2017. “Defects monitoring of laser metal
deposition using acoustic emission sensor.” International Journal
of Advanced Manufacturing Technology 90 (1-4): 561–74.
https://doi.org/10.1007/s00170-016-9366-x
17. Wu, M., V. V. Phoha, Y. B. Moon, and A. K. Belman. 2016. “Detecting
malicious defects in 3d printing process using machine learning and
image classification.” Proceedings of the ASME 2016 International
Mechanical Engineering Congress and Exposition (14): V014T07A004.
https://doi.org/10.1115/IMECE2016-67641
18. Bacha, A., A. H. Sabry, and J. Benhra. 2019. “Fault Diagnosis in the
Field of Additive Manufacturing (3D Printing) Using Bayesian Networks.”
International Journal of Online &Biomedical Engineering 15 (3): 110–23.
https://doi.org/10.3991/ijoe.v15i03.9375
19. Wang, C., X. Tan, S. Tor, and C. Lim. 2020. “Machine learning in
additive manufacturing: State-of-the-art and perspectives.” Additive
Manufacturing 36: 101538. https://doi.org/10.1016/j.addma.2020.101538
20. Meng, L., B. McWilliams, W. Jarosinski, H.-Y. Park, Y.-G. Jung, J. Lee,
and J. Zhang. 2020. “Machine learning in additive manufacturing:
A review.” Journal of the Minerals Metals &Materials Society 72 (6):
2363–77. https://doi.org/10.1007/s11837-020-04155-y
21. Menon, A., B. Póczos, A. W. Feinberg, and N. R. Washburn. 2019.
“Optimization of silicone 3D printing with hierarchical machine
learning.” 3D Printing and Additive Manufacturing 6 (4): 181–189.
https://doi.org/10.1089/3dp.2018.0088
22. Mohammadi, M. G., D. Mahmoud, and M. Elbestawi. 2021.
“On the application of machine learning for defect detection in L-PBF
additive manufacturing.” Optics &Laser Technology 143: 107338.
https://doi.org/10.1016/j.optlastec.2021.107338
23. Scime, L., and J. Beuth. 2018. “A multi-scale convolutional neural
network for autonomous anomaly detection and classification in a laser
powder bed fusion additive manufacturing process.” Additive Manufac-
turing 24: 273–86. https://doi.org/10.1016/j.addma.2018.09.034
J U LY 2 0 2 6 • M AT E R I A L S E V A L U AT I O N 51
