Page 122 - MSAM-4-3

Materials Science in Additive Manufacturing Sustainable manufacturing composite material optimization

learning-based recommendation framework for material doi: 10.3390/s24092668
extrusion fabricated triply periodic minimal surface lattice 15. Liu S, Yang C. Machine learning design for high-entropy
structures. J Mater Sci Mater Eng. 2025;20(1);27.
alloys: Models and algorithms. Metals. 2024;14(2):235.
doi: 10.1186/s40712-025-00229-4
doi: 10.3390/met14020235
5. Goh GD, Sing SL, Lim YF, et al. Machine learning for 3D 16. Ng WL, Goh GL, Goh GD, Ten JSJ, Yeong WY. Progress
printed multi-materials tissue-mimicking anatomical and opportunities for machine learning in materials
models. Mater Design. 2021;211:110125.
and processes of additive manufacturing. Adv Mater.
doi: 10.1016/j.matdes.2021.110125 2024;36(34):2310006.
6. Yadav R. Analytic hierarchy process‐technique for doi: 10.1002/adma.202310006
order preference by similarity to ideal solution: A multi 17. Fahimi K, Amirabadi M. Constructing the organizational
criteria decision‐making technique to select the best excellence model using technique for order of preference by
dental restorative composite materials. Polym Compos. similarity to ideal solution and Analytic hierarchy process.
2021;42(12):6867-6877. Int J Hum Capital Urban Manag. 2024;9:157-176.
doi: 10.1002/pc.26346 doi: 10.22034/IJHCUM.2024.01.11
7. Gyani J, Ahmed A, Haq MA. MCDM and various 18. Galal A, Elawady H, Mostafa NA. An integrated framework
prioritization methods in AHP for CSS: A comprehensive for third party logistic evaluation by using fuzzy analytical
review. IEEE Access. 2022;10:33492-33511.
hierarchy process and technique for order preference
doi: 10.1109/ACCESS.2022.3161742 by similarity to ideal solution. Int J Logist Syst Manag.
2025;50(3):361-385.
8. Tran NT, Trinh VL, Chung CK. An integrated approach of
fuzzy AHP-TOPSIS for multi-criteria decision-making in doi: 10.1504/IJLSM.2025.144680
industrial robot selection. Processes. 2024;12(8):1723. 19. Prasetyo DE, Nurfaizal H, Effendi A. Comparative analysis
doi: 10.3390/pr12081723 of the analytical hierarchy process (ahp) and technique
for order preference by similarity to ideal solution (topsis)
9. Kantaros A, Katsantoni M, Ganetsos T, Petrescu N. The methods in selecting majors for new students: A case study
evolution of thermoplastic raw materials in high-speed
FFF/FDM 3D printing Era: Challenges and opportunities. at smks binong permai an-nurmaniyah. J Inform Utama.
Materials (Basel). 2025;18(6):1220. 2024;2(1):43-49.
20. Hanafi AM, Moawed MA, Abdellatif OE. Advancing
doi: 10.3390/ma18061220
sustainable energy management: A comprehensive review
10. Achite M, Nasiri H, Katipoğlu OM, Abdallah M, of artificial intelligence techniques in building. Eng Res J
Moazenzadeh R, Mohammadi B. A coupled extreme gradient (Shoubra). 2024;53(2):26-46.
boosting-MPA approach for estimating daily reference doi: 10.21608/erjsh.2023.226854.1196
evapotranspiration. Theor Appl Climatol. 2025;156(2):113.
21. Ukoba K, Olatunji KO, Adeoye E, Jen TC, Madyira DM.
doi: 10.1007/s00704-024-05313-x
Optimizing renewable energy systems through artificial
11. Nandipati M, Fatoki O, Desai S. Bridging nanomanufacturing intelligence: Review and future prospects. Energy Environ.
and artificial intelligence-a comprehensive review. Materials 2024;35(7):3833-3879.
(Basel). 2024;17(7):1621.
doi: 10.1177/0958305X241256293
doi: 10.3390/ma17071621
22. Rojek I, Mikołajewski D, Mroziński A, Macko M. Green
12. Batu T, Lemu HG, Shimels H. Application of artificial energy management in manufacturing based on demand
intelligence for surface roughness prediction of prediction by artificial intelligence-a review. Electronics.
additively manufactured components. Materials (Basel). 2024;13(16):3338.
2023;16(18):6266.
doi: 10.3390/electronics13163338
doi: 10.3390/ma16186266
23. Sarkar C, Das B, Rawat VS, et al. Artificial intelligence and
13. Elahi M, Afolaranmi SO, Lastra JLM, Garcia JAP. machine learning technology driven modern drug discovery
A comprehensive literature review of the applications of AI and development. Int J Mol Sci. 2023;24(3):2026.
techniques through the lifecycle of industrial equipment. doi: 10.3390/ijms24032026
Discov Artif Intell. 2023;3:43.
24. Babu SS, Mourad AHI, Harib KH, Vijayavenkataraman S.
doi: 10.1007/s44163-023-00089-x
Recent developments in the application of machine-learning
14. Zhou L, Miller J, Vezza J, et al. Additive manufacturing: towards accelerated predictive multiscale design and additive
A comprehensive review. Sensors (Basel). 2024;24(9):2668. manufacturing. Virtual Phys Prototy. 2023;18(1):e2141653.

Volume 4 Issue 3 (2025) 14 doi: 10.36922/MSAM025200033