24. Raza, A., A. Haider, and W. Haider. 2021. “A Consolidated Approach
towards Application of Machine Learning Principles in Additive Manufac-
turing.” In 2021 IEEE International Conference on Electro Information Tech-
nology (EIT), pp. 1–6. IEEE. https://doi.org/10.1109/EIT51626.2021.9491833
25. Ogunsanya, M., J. Isichei, S. K. Parupelli, S. Desai, and Y. Cai. 2021.
“In-situ droplet monitoring of inkjet 3D printing process using image
analysis and machine learning models.” Procedia Manufacturing 53:
427–34. https://doi.org/10.1016/j.promfg.2021.06.045
26. Malik, S., and A. Kontsos. 2024. “Artificial intelligence for materials
damage diagnostics and prognostics.” In Artificial Intelligence in Manufac-
turing, eds. Soroush, M. and R. D. Braatz, pp. 265–306. Academic Press.
https://doi.org/10.1016/B978-0-323-99134-6.00004-9
27. Hall, D. L., and J. Llinas. 1997. “An introduction to multisensor
data fusion.” Proceedings of the IEEE 85 (1): 6–23.
https://doi.org/10.1109/5.554205
28. Liggins, M., II, D. Hall, and J. Llinas (eds.). 2017. Handbook
of multisensor data fusion: theory and practice. CRC Press.
https://doi.org/10.1201/9781420053098
29. Peng, X., L. Kong, W. Han, and S. Wang. 2022. “Multi-Sensor Image
Fusion Method for Defect Detection in Powder Bed Fusion.” Sensors
(Basel) 22 (20): 8023. https://doi.org/10.3390/s22208023
30. Rescsanski, S., M. Imani, and F. Imani. 2022. “Heterogeneous
Sensing and Bayesian Optimization for Smart Calibration in Additive
Manufacturing Process.” In ASME 2022 International Mechanical
Engineering Congress and Exposition: V02BT02A051.
https://doi.org/10.1115/IMECE2022-96010
31. Grasso, M., F. Gallina, and B. M. Colosimo. 2018. “Data fusion
methods for statistical process monitoring and quality characterization
in metal additive manufacturing.” Procedia CIRP 75: 103–07.
https://doi.org/10.1016/j.procir.2018.04.045
32. Yang, Z., Y. Lu, S. Li, J. Li, Y. Ndiaye, H. Yang, and S. Krishnamurty.
2021. “In-process data fusion for process monitoring and control of metal
additive manufacturing.” In ASME 2021 International Design Engineering
Technical Conferences and Computers and Information in Engineering
Conference: V002T02A072. https://doi.org/10.1115/DETC2021-71813
33. Gaikwad, A., B. Giera, G. M. Guss, J.-B. Forien, M. J. Matthews,
and P. Rao. 2020. “Heterogeneous sensing and scientific
machine learning for quality assurance in laser powder bed
fusion–A single-track study.” Additive Manufacturing 36: 101659.
https://doi.org/10.1016/j.addma.2020.101659
34. Vandone, A., S. Baraldo, and A. Valente. 2018. “Multisensor data fusion
for additive manufacturing process control.” IEEE Robotics and Automa-
tion Letters 3 (4): 3279–84. https://doi.org/10.1109/LRA.2018.2851792
35. Petrich, J., Z. Snow, D. Corbin, and E. W. Reutzel. 2021. “Multi-modal
sensor fusion with machine learning for data-driven process monitoring
for additive manufacturing.” Additive Manufacturing 48: 102364.
https://doi.org/10.1016/j.addma.2021.102364
36. Wu, H., Z. Yu, and Y. Wang. 2016. “A new approach for online moni-
toring of additive manufacturing based on acoustic emission.” In ASME
2016 11th International Manufacturing Science and Engineering Conference:
V003T08A013. https://doi.org/10.1115/MSEC2016-8551
37. Garanger, K., T. Khamvilai, and E. Feron. 2018. “3D printing
of a leaf spring: A demonstration of closed-loop control in additive
manufacturing.” 2018 IEEE Conference on Control Technology and
Applications (CCTA), Copenhagen, Denmark, pp. 465–470.
https://doi.org/10.1109/CCTA.2018.8511509
38. Reese, R., H. Bheda, and W. Mondesir. 2019. “Method to Monitor
Additive Manufacturing Process for Detection and In-Situ Correction
of Defects.” US Patent US10421267B2, issued 2019.
https://patents.google.com/patent/US10421267B2/en
39. Wang, T., T.-H. Kwok, C. Zhou, and S. Vader. 2018. “In-situ droplet
inspection and closed-loop control system using machine learning for
liquid metal jet printing.” Journal of Manufacturing Systems 47: 83–92.
https://doi.org/10.1016/j.jmsy.2018.04.003
40. Breese, P. P., T. Hauser, D. Regulin, S. Seebauer, and C. Rupprecht.
2021. “In situ measurement and closed-loop control for powder supply
processes.” International Journal of Advanced Manufacturing Technology
116 (3–4): 889–903. https://doi.org/10.1007/s00170-021-07438-z
41. Rivera, F. J. M., and A. J. R. Arciniegas. 2020. “Additive manufacturing
methods: Techniques, materials, and closed-loop control applications.”
International Journal of Advanced Manufacturing Technology 109 (1–2):
17–31. https://doi.org/10.1007/s00170-020-05663-6
42. Malik, S., R. Rouf, K. Mazur, and A. Kontsos. 2020. “The industry
Internet of Things (IIoT) as a methodology for autonomous diagnostics in
aerospace structural health monitoring.” Aerospace (Basel, Switzerland)
7 (5): 64. https://doi.org/10.3390/aerospace7050064
43. Malik, S., R. Rouf, K. Mazur, and A. Kontsos. 2020. “A Dynamic Data
Driven Applications Systems (DDDAS)-Based Digital Twin IoT Frame-
work.” In Dynamic Data Driven Applications Systems, eds. Darema, F.,
E. Blasch, S. Ravela, and A. Aved, pp. 29–36. DDDAS 2020, Boston, MA.
Springer Cham. https://doi.org/10.1007/978-3-030-61725-7_6
44. Malik, S., K. Mazur, R. Rouf, and A. Kontsos. 2019. “The industry
internet of things (IIoT) as a methodology for autonomous diagnostics,
prognostics in aerospace structural health monitoring.” Structural Health
Monitoring 2019. https://doi.org/10.12783/shm2019/32214
45. Menéndez, M., J. Pardo, L. Pardo, and M. Pardo. 1997. “The jensen-
shannon divergence.” Journal of the Franklin Institute 334 (2): 307–18.
https://doi.org/10.1016/S0016-0032(96)00063-4
46. Malik, S., and A. Kontsos. 2022. “An Information Theory Approach for
Internet of Things Enabled Damage Monitoring.” In ASME 2022 Confer-
ence on Smart Materials, Adaptive Structures and Intelligent Systems:
V001T05A008. https://doi.org/10.1115/SMASIS2022-91119
47. Malik, S., and A. Kontsos. 2023. “Implementation of Information
Entropy in an Industrial Internet of Things Approach for Structural
Health Monitoring Applications.” Structural Health Monitoring 2023.
https://doi.org/10.12783/shm2023/36907
48. Baldi, P., and P. J. Sadowski. 2013. Understanding dropout.”
Advances in Neural Information Processing Systems 26: 2814–2822.
https://proceedings.neurips.cc/paper/2013/hash/71f6278d140a
f599e06ad9bf1ba03cb0-Abstract.html
49. Eren, L., T. Ince, and S. Kiranyaz. 2019. “A generic intelligent bearing
fault diagnosis system using compact adaptive 1D CNN classifier.” Journal
of Signal Processing Systems for Signal, Image, and Video Technology 91 (2):
179–89. https://doi.org/10.1007/s11265-018-1378-3
50. He, J., X. Li, Y. Chen, D. Chen, J. Guo, and Y. Zhou. 2021. “Deep transfer
learning method based on 1D-CNN for bearing fault diagnosis.” Shock and
Vibration 2021: 6687331. https://doi.org/10.1155/2021/6687331
51. Ciaburro, G., and G. Iannace. 2022. “Machine-learning-based methods
for acoustic emission testing: A review.” Applied Sciences (Basel, Switzer-
land) 12 (20): 10476. https://doi.org/10.3390/app122010476
52. Essien, A., and C. Giannetti. 2020. “A deep learning model for smart
manufacturing using convolutional LSTM neural network autoencoders.”
IEEE Transactions on Industrial Informatics 16 (9): 6069–78.
https://doi.org/10.1109/TII.2020.2967556
53. Imoto, K., T. Nakai, T. Ike, K. Haruki, and Y. Sato. 2019. “A CNN-based
transfer learning method for defect classification in semiconductor manu-
facturing.” IEEE Transactions on Semiconductor Manufacturing 32 (4):
455–59. https://doi.org/10.1109/TSM.2019.2941752
ME
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52
M AT E R I A L S E V A L U AT I O N • J U LY 2 0 2 6
towards Application of Machine Learning Principles in Additive Manufac-
turing.” In 2021 IEEE International Conference on Electro Information Tech-
nology (EIT), pp. 1–6. IEEE. https://doi.org/10.1109/EIT51626.2021.9491833
25. Ogunsanya, M., J. Isichei, S. K. Parupelli, S. Desai, and Y. Cai. 2021.
“In-situ droplet monitoring of inkjet 3D printing process using image
analysis and machine learning models.” Procedia Manufacturing 53:
427–34. https://doi.org/10.1016/j.promfg.2021.06.045
26. Malik, S., and A. Kontsos. 2024. “Artificial intelligence for materials
damage diagnostics and prognostics.” In Artificial Intelligence in Manufac-
turing, eds. Soroush, M. and R. D. Braatz, pp. 265–306. Academic Press.
https://doi.org/10.1016/B978-0-323-99134-6.00004-9
27. Hall, D. L., and J. Llinas. 1997. “An introduction to multisensor
data fusion.” Proceedings of the IEEE 85 (1): 6–23.
https://doi.org/10.1109/5.554205
28. Liggins, M., II, D. Hall, and J. Llinas (eds.). 2017. Handbook
of multisensor data fusion: theory and practice. CRC Press.
https://doi.org/10.1201/9781420053098
29. Peng, X., L. Kong, W. Han, and S. Wang. 2022. “Multi-Sensor Image
Fusion Method for Defect Detection in Powder Bed Fusion.” Sensors
(Basel) 22 (20): 8023. https://doi.org/10.3390/s22208023
30. Rescsanski, S., M. Imani, and F. Imani. 2022. “Heterogeneous
Sensing and Bayesian Optimization for Smart Calibration in Additive
Manufacturing Process.” In ASME 2022 International Mechanical
Engineering Congress and Exposition: V02BT02A051.
https://doi.org/10.1115/IMECE2022-96010
31. Grasso, M., F. Gallina, and B. M. Colosimo. 2018. “Data fusion
methods for statistical process monitoring and quality characterization
in metal additive manufacturing.” Procedia CIRP 75: 103–07.
https://doi.org/10.1016/j.procir.2018.04.045
32. Yang, Z., Y. Lu, S. Li, J. Li, Y. Ndiaye, H. Yang, and S. Krishnamurty.
2021. “In-process data fusion for process monitoring and control of metal
additive manufacturing.” In ASME 2021 International Design Engineering
Technical Conferences and Computers and Information in Engineering
Conference: V002T02A072. https://doi.org/10.1115/DETC2021-71813
33. Gaikwad, A., B. Giera, G. M. Guss, J.-B. Forien, M. J. Matthews,
and P. Rao. 2020. “Heterogeneous sensing and scientific
machine learning for quality assurance in laser powder bed
fusion–A single-track study.” Additive Manufacturing 36: 101659.
https://doi.org/10.1016/j.addma.2020.101659
34. Vandone, A., S. Baraldo, and A. Valente. 2018. “Multisensor data fusion
for additive manufacturing process control.” IEEE Robotics and Automa-
tion Letters 3 (4): 3279–84. https://doi.org/10.1109/LRA.2018.2851792
35. Petrich, J., Z. Snow, D. Corbin, and E. W. Reutzel. 2021. “Multi-modal
sensor fusion with machine learning for data-driven process monitoring
for additive manufacturing.” Additive Manufacturing 48: 102364.
https://doi.org/10.1016/j.addma.2021.102364
36. Wu, H., Z. Yu, and Y. Wang. 2016. “A new approach for online moni-
toring of additive manufacturing based on acoustic emission.” In ASME
2016 11th International Manufacturing Science and Engineering Conference:
V003T08A013. https://doi.org/10.1115/MSEC2016-8551
37. Garanger, K., T. Khamvilai, and E. Feron. 2018. “3D printing
of a leaf spring: A demonstration of closed-loop control in additive
manufacturing.” 2018 IEEE Conference on Control Technology and
Applications (CCTA), Copenhagen, Denmark, pp. 465–470.
https://doi.org/10.1109/CCTA.2018.8511509
38. Reese, R., H. Bheda, and W. Mondesir. 2019. “Method to Monitor
Additive Manufacturing Process for Detection and In-Situ Correction
of Defects.” US Patent US10421267B2, issued 2019.
https://patents.google.com/patent/US10421267B2/en
39. Wang, T., T.-H. Kwok, C. Zhou, and S. Vader. 2018. “In-situ droplet
inspection and closed-loop control system using machine learning for
liquid metal jet printing.” Journal of Manufacturing Systems 47: 83–92.
https://doi.org/10.1016/j.jmsy.2018.04.003
40. Breese, P. P., T. Hauser, D. Regulin, S. Seebauer, and C. Rupprecht.
2021. “In situ measurement and closed-loop control for powder supply
processes.” International Journal of Advanced Manufacturing Technology
116 (3–4): 889–903. https://doi.org/10.1007/s00170-021-07438-z
41. Rivera, F. J. M., and A. J. R. Arciniegas. 2020. “Additive manufacturing
methods: Techniques, materials, and closed-loop control applications.”
International Journal of Advanced Manufacturing Technology 109 (1–2):
17–31. https://doi.org/10.1007/s00170-020-05663-6
42. Malik, S., R. Rouf, K. Mazur, and A. Kontsos. 2020. “The industry
Internet of Things (IIoT) as a methodology for autonomous diagnostics in
aerospace structural health monitoring.” Aerospace (Basel, Switzerland)
7 (5): 64. https://doi.org/10.3390/aerospace7050064
43. Malik, S., R. Rouf, K. Mazur, and A. Kontsos. 2020. “A Dynamic Data
Driven Applications Systems (DDDAS)-Based Digital Twin IoT Frame-
work.” In Dynamic Data Driven Applications Systems, eds. Darema, F.,
E. Blasch, S. Ravela, and A. Aved, pp. 29–36. DDDAS 2020, Boston, MA.
Springer Cham. https://doi.org/10.1007/978-3-030-61725-7_6
44. Malik, S., K. Mazur, R. Rouf, and A. Kontsos. 2019. “The industry
internet of things (IIoT) as a methodology for autonomous diagnostics,
prognostics in aerospace structural health monitoring.” Structural Health
Monitoring 2019. https://doi.org/10.12783/shm2019/32214
45. Menéndez, M., J. Pardo, L. Pardo, and M. Pardo. 1997. “The jensen-
shannon divergence.” Journal of the Franklin Institute 334 (2): 307–18.
https://doi.org/10.1016/S0016-0032(96)00063-4
46. Malik, S., and A. Kontsos. 2022. “An Information Theory Approach for
Internet of Things Enabled Damage Monitoring.” In ASME 2022 Confer-
ence on Smart Materials, Adaptive Structures and Intelligent Systems:
V001T05A008. https://doi.org/10.1115/SMASIS2022-91119
47. Malik, S., and A. Kontsos. 2023. “Implementation of Information
Entropy in an Industrial Internet of Things Approach for Structural
Health Monitoring Applications.” Structural Health Monitoring 2023.
https://doi.org/10.12783/shm2023/36907
48. Baldi, P., and P. J. Sadowski. 2013. Understanding dropout.”
Advances in Neural Information Processing Systems 26: 2814–2822.
https://proceedings.neurips.cc/paper/2013/hash/71f6278d140a
f599e06ad9bf1ba03cb0-Abstract.html
49. Eren, L., T. Ince, and S. Kiranyaz. 2019. “A generic intelligent bearing
fault diagnosis system using compact adaptive 1D CNN classifier.” Journal
of Signal Processing Systems for Signal, Image, and Video Technology 91 (2):
179–89. https://doi.org/10.1007/s11265-018-1378-3
50. He, J., X. Li, Y. Chen, D. Chen, J. Guo, and Y. Zhou. 2021. “Deep transfer
learning method based on 1D-CNN for bearing fault diagnosis.” Shock and
Vibration 2021: 6687331. https://doi.org/10.1155/2021/6687331
51. Ciaburro, G., and G. Iannace. 2022. “Machine-learning-based methods
for acoustic emission testing: A review.” Applied Sciences (Basel, Switzer-
land) 12 (20): 10476. https://doi.org/10.3390/app122010476
52. Essien, A., and C. Giannetti. 2020. “A deep learning model for smart
manufacturing using convolutional LSTM neural network autoencoders.”
IEEE Transactions on Industrial Informatics 16 (9): 6069–78.
https://doi.org/10.1109/TII.2020.2967556
53. Imoto, K., T. Nakai, T. Ike, K. Haruki, and Y. Sato. 2019. “A CNN-based
transfer learning method for defect classification in semiconductor manu-
facturing.” IEEE Transactions on Semiconductor Manufacturing 32 (4):
455–59. https://doi.org/10.1109/TSM.2019.2941752
ME
|
DIGITALTHREAD
52
M AT E R I A L S E V A L U AT I O N • J U LY 2 0 2 6
