Page 44 - IJAMD-1-2
P. 44
International Journal of AI for
Materials and Design
AI-driven quality assurance in AM
Acknowledgments 5. Plathottam SJ, Rzonca A, Lakhnori R, Iloeje CO. A review
of artificial intelligence applications in manufacturing
We would like to express our sincere gratitude to all operations. J Adv Manuf Process. 2023;5(3):e10159.
individuals and organizations who contributed to the doi: 10.1002/amp2.10159
completion of this review article. We are thankful to
the participants and volunteers who participated in 6. Sahoo SK, Goswami SS, Sarkar S, Mitra S. A review of
data collection and experimentation. Finally, we are digital transformation and industry 4.0 in supply chain
grateful to our families and friends for their unwavering management for small and medium-sized enterprises. Spectr
encouragement and understanding during this endeavor. Eng Manag Sci. 2023;1(1):58-72.
doi: 10.31181/sems1120237j
Funding
7. Elahi M, Afolaranmi SO, Lastra JL, Perez Garcia JA.
None. A comprehensive literature review of the applications of AI
techniques through the lifecycle of industrial equipment.
Conflict of interest Discov Artif Intell. 2023;3(1):43.
The authors declare that they have no competing interests. doi: 10.1007/s44163-023-00089-x
8. Sahoo SK, Goswami SS, Halder, R. Supplier selection in the
Author contributions age of industry 4.0: A review on MCDM applications and
Conceptualization: Shankha Shubhra Goswami trends. Decis Mak Adv. 2024;2(1):32-47.
Writing – original draft: Surajit Mondal doi: 10.31181/dma21202420
Writing – review & editing: Shankha Shubhra Goswami
9. Kim H, Lin Y, Tseng TL. A review on quality control in additive
Ethics approval and consent to participate manufacturing. Rapid Prototyp J. 2018;24(3):645-669.
doi: 10.1108/RPJ-03-2017-0048
Not applicable.
10. Qin J, Hu F, Liu Y, et al. Research and application of
Consent for publication machine learning for additive manufacturing. Addit Manuf.
2022;52:102691.
Not applicable.
doi: 10.1016/j.addma.2022.102691
Availability of data 11. Bonatti AF, Vozzi G, De Maria C. Enhancing quality control
Not applicable. in bioprinting through machine learning. Biofabrication.
2024;16(2):022001.
References doi: 10.1088/1758-5090/ad2189
1. Balhara H, Karthikeyan A, Hanchate A, Nakkina TG, 12. Jin Z, Zhang Z, Gu GX. Automated real‐time detection
Bukkapatnam ST. Imaging systems and techniques for and prediction of interlayer imperfections in additive
fusion-based metal additive manufacturing: A review. Front manufacturing processes using artificial intelligence. Adv
Manuf Technol. 2023;3:1271190. Intell Syst. 2020;2(1):1900130.
doi: 10.3389/fmtec.2023.1271190 doi: 10.1002/aisy.201900130
2. Goswami SS, Sarkar S, Gupta KK, Mondal S. The role of 13. Bikas H, Terzakis MA, Stavropoulos P. Manufacturability-
cyber security in advancing sustainable digitalization: based design optimization for directed energy deposition
Opportunities and challenges. J Decis Anal Intell Comput. processes. Machines. 2023;11(9):879.
2023;3(1):270-285.
doi: 10.3390/machines11090879
doi: 10.31181/jdaic10018122023g
14. Stavropoulos P, Tzimanis K, Souflas T, Bikas H. Knowledge-
3. Ionașcu AE, Goswami SS, Dănilă A, Horga MG, Barbu C, based manufacturability assessment for optimization of
Adrian ŞC. Analyzing primary sector selection for economic additive manufacturing processes based on automated
activity in Romania: An interval-valued fuzzy multi-criteria feature recognition from CAD models. Int J Adv Manuf
approach. Mathematics. 2024;12(8):1157. Technol. 2022;122(2):993-1007.
doi: 10.3390/math12081157 doi: 10.1007/s00170-022-09948-w
4. Sahoo SK, Goswami SS. Green supplier selection using 15. Wang C, Tan XP, Tor SB, Lim CS. Machine learning in
MCDM: A comprehensive review of recent studies. Spectr additive manufacturing: State-of-the-art and perspectives.
Eng Manag Sci. 2024;2(1):1-16. Addit Manuf. 2020;36:101538.
doi: 10.31181/sems1120241a doi: 10.1016/j.addma.2020.101538
Volume 1 Issue 2 (2024) 38 doi: 10.36922/ijamd.3455

