Page 30 - IJAMD-2-3

International Journal of AI for
Materials and Design AI applications in composite materials

7. Soori M, Arezoo B, Dastres R. Artificial intelligence, Mouritz AP. Energy storage structural composites with
machine learning and deep learning in advanced robotics, a integrated lithium‐ion batteries: A review. Adv Mater
review. Cogn Robot. 2023;3:54-70. Technol. 2021;6(8):2001059.
doi: 10.1016/j.cogr.2023.04.001 doi: 10.1002/admt.202001059
8. Brunton SL, Nathan Kutz J, Manohar K, et al. Data-driven 19. Resor BR. Definition of a 5MW/61.5 m Wind Turbine Blade
aerospace engineering: Reframing the industry with Reference Model. California: Sandia National Laboratories; 2013.
machine learning. Aiaa J. 2021;59(8):2820-2847.
20. Moein MM, Saradar A, Rahmati K, et al. Predictive models
doi: 10.2514/1.J060131 for concrete properties using machine learning and deep
9. Lingitz L, Gallina V, Ansari F, et al. Lead time prediction using learning approaches: A review. J Build Eng. 2023;63:105444.
machine learning algorithms: A case study by a semiconductor doi: 10.1016/j.jobe.2022.105444
manufacturer. Procedia CIRP. 2018;72:1051-1056.
21. Dijkstra M, Luijten E. From predictive modelling to machine
doi: 10.1016/j.procir.2018.03.148 learning and reverse engineering of colloidal self-assembly.
10. Hong H, Kim S, Kim W, Kim W, Jeong JM, Kim SS. Nat Mater. 2021;20(6):762-773.
Design optimization of 3D printed kirigami-inspired doi: 10.1038/s41563-021-01014-2
composite metamaterials for quasi-zero stiffness using
deep reinforcement learning integrated with bayesian 22. Sengar SS, Hasan AB, Kumar S, Carroll F. Generative
optimization. Compos Struct. 2025;359:119031. artificial intelligence: A systematic review and applications.
Multimed Tools Appl. 2024;84:1-40.
doi: 10.1016/j.compstruct.2025.119031
doi: 10.1007/s11042-024-20016-1
11. Hong H, Kim W, Kim W, Jeong JM, Kim S, Kim SS.
Machine learning-driven design optimization of buckling- 23. Sarker IH. AI-based modeling: Techniques, applications and
induced quasi-zero stiffness metastructures for low- research issues towards automation, intelligent and smart
frequency vibration isolation. ACS Appl Mater Interfaces. systems. SN Comput Sci. 2022;3(2):158.
2024;16(14):17965-17972. doi: 10.1007/s42979-022-01043-x
doi: 10.1021/acsami.3c18793 24. Agatonovic-Kustrin S, Beresford R. Basic concepts
12. Hong H, Jeong KI, On SY, Kim W, Kim SS. Structural of artificial neural network (ANN) modeling and its
optimization of an arch-structured epoxy/rubber composite application in pharmaceutical research. J Pharm Biomed
vibration isolator using deep Q-value neural network Anal. 2000;22(5):717-727.
reinforcement learning. Compos Struct. 2023;323:117506. doi: 10.1016/S0731-7085(99)00272-1
doi: 10.1016/j.compstruct.2023.117506 25. Abiodun OI, Jantan A, Omolara AE, Dada KV,
13. Barile C, Casavola C, De Cillis F. Mechanical comparison of Mohamed NA, Arshad H. State-of-the-art in artificial neural
new composite materials for aerospace applications. Compos network applications: A survey. Heliyon. 2018;4(11):e00938.
Part B Eng. 2019;162:122-128. doi: 10.1016/j.heliyon.2018.e00938
doi: 10.1016/j.compositesb.2018.10.101 26. Sze V, Chen YH, Yang TJ, Emer JS. Efficient processing of
14. Lee J, Lee D, Park J, Choi I, Lim JW, Kim S. Carbon/epoxy deep neural networks: A tutorial and survey. Proceed IEEE.
composite foot structure for biped robots. Compos Struct. 2017;105(12):2295-2329.
2016;140:344-350. doi: 10.1109/JPROC.2017.2761740
doi: 10.1016/j.compstruct.2016.01.022 27. Samek W, Montavon G, Lapuschkin S, Anders CJ, Müller KR.
15. Bank LC. Composites for Construction: Structural Design Explaining deep neural networks and beyond: A review of
with FRP Materials. United States: John Wiley and Sons; methods and applications. Proceed IEEE. 2021;109(3):247-278.
2006. doi: 10.1109/JPROC.2021.3060483
16. Sarfraz MS, Hong H, Kim SS. Recent developments in the 28. Larochelle H, Bengio Y, Louradour J, Lamblin P. Exploring
manufacturing technologies of composite components and strategies for training deep neural networks. J Mach Learn
their cost-effectiveness in the automotive industry: A review Res. 2009;10(1):1-40.
study. Compos Struct. 2021;266:113864.
29. Hong H, Sarfraz MS, Jeong M, et al. Prediction of ground
doi: 10.1016/j.compstruct.2021.113864
reaction forces using the artificial neural network from
17. Mrazova M. Advanced composite materials of the future in capacitive self-sensing values of composite ankle springs for
aerospace industry. Incas Bull. 2013;5(3):139-50. exo-robots. Compos Struct. 2022;301:116233.
18. Galos J, Pattarakunnan K, Best AS, Kyratzis IL, Wang CH, doi: 10.1016/j.compstruct.2022.116233

Volume 2 Issue 3 (2025) 24 doi: 10.36922/IJAMD025210016