Page 32 - IJAMD-2-3

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

neural networks. Compos Struct. 2019;227:111264. doi: 10.1016/j.compositesb.2024.111751
doi: 10.1016/j.compstruct.2019.111264 64. Liu C, Li X, Ge J, et al. A deep learning framework based
on attention mechanism for predicting the mechanical
53. Shorten C, Khoshgoftaar TM. A survey on image data
augmentation for deep learning. J Big Data. 2019;6(1):60. properties and failure mode of embedded wrinkle fiber-
reinforced composites. Compos Part A Appl Sci Manuf.
doi: 10.1186/s40537-019-0197-0 2024;186:108401.
54. Mikołajczyk A, Grochowski M. Data Augmentation for doi: 10.1016/j.compositesa.2024.108401
Improving Deep Learning in Image Classification Problem.
New York: IEEE; 2018. p. 117-122. 65. Yousefi E, Shiri MB, Rezaei MA, Rezaei S, Band SS,
Mosavi A. A novel long-term water absorption and
55. Shim YB, Lee IY, Park YB. Predicting the material thickness swelling deep learning forecast method for corn
behavior of recycled composites: Experimental analysis husk fiber-polypropylene composite. Case Stud Construct
and deep learning hybrid approach. Compos Sci Technol. Mater. 2022;17:e01268.
2024;249:110464.
doi: 10.1016/j.cscm.2022.e01268
doi: 10.1016/j.compscitech.2024.110464
66. Gao H, Zhai Y, Wang T. A deep LSTM-based constitutive
56. Zhu X, Liu Y, Li J, Wan T, Qin Z. Emotion Classification with model for describing the impact characteristics of concrete-
Data Augmentation using Generative Adversarial Networks. granite composites with different roughness interfaces. Sci
Berlin: Springer; 2018. p. 349-360. Rep. 2024;14(1):29129.
57. Lata K, Dave M, Nishanth KN. Data Augmentation doi: 10.1038/s41598-024-80366-6
using Generative Adversarial Network. In: Proceedings of
2 International Conference on Advanced Computing and 67. Cheung HL, Mirkhalaf M. A multi-fidelity data-driven
nd
Software Engineering (ICACSE); 2019. model for highly accurate and computationally efficient
modeling of short fiber composites. Compos Sci Technol.
58. Shao S, Wang P, Yan R. Generative adversarial networks for 2024;246:110359.
data augmentation in machine fault diagnosis. Comput Ind.
2019;106:85-93. doi: 10.1016/j.compscitech.2023.110359
doi: 10.1016/j.compind.2019.01.001 68. El Said B. Predicting the non-linear response of composite
materials using deep recurrent convolutional neural
59. Liu C, Xu X, Wu J, Zhu H, Wang C. Deep transfer learning- networks. Int J Solids Struct. 2023;276:112334.
based damage detection of composite structures by fusing
monitoring data with physical mechanism. Eng Appl Artif doi: 10.1016/j.ijsolstr.2023.112334
Intell. 2023;123:106245. 69. Maia M, Rocha IB, Kovačević D, Van der Meer F. Physically
doi: 10.1016/j.engappai.2023.106245 recurrent neural network for rate and path-dependent
heterogeneous materials in a finite strain framework. Mech
60. Xu Y, Weng H, Ju X, et al. A method for predicting Mater. 2024;198:105145.
mechanical properties of composite microstructure with
reduced dataset based on transfer learning. Compos Struct. doi: 10.1016/j.mechmat.2024.105145
2021;275:114444. 70. Chen Q, Jia R, Pang S. Deep long short-term memory
doi: 10.1016/j.compstruct.2021.114444 neural network for accelerated elastoplastic analysis
of heterogeneous materials: An integrated data-driven
61. Yi J, Deng B, Peng F, et al. Study on the parameters surrogate approach. Compos Struct. 2021;264:113688.
optimization of 3D printing continuous carbon fiber-
reinforced composites based on CNN and NSGA-II. Compos doi: 10.1016/j.compstruct.2021.113688
Part A Appl Sci Manuf. 2025;190:108657. 71. Borkowski L, Skinner T, Chattopadhyay A. Woven ceramic
doi: 10.1016/j.compositesa.2024.108657 matrix composite surrogate model based on physics-
informed recurrent neural network. Compos Struct.
62. Yu C, Zheng S, Zhao X. A novel version of hierarchical
genetic algorithm and its application for hyperparameters 2023;305:116455.
optimization in CNN models for structural delamination doi: 10.1016/j.compstruct.2022.116455
identification. J Braz Soc Mech Sci Eng. 2024;46(8):462. 72. Arnold SM, Mital SK, Hearley BL. Stiffness and Fatigue Life
doi: 10.21203/rs.3.rs-3620270/v1 Estimator for Polymer Composite Laminates Using Machine
Learning. Ohio: Glenn Research Center; 2023.
63. DeMille KJ, Hall R, Leigh JR, Guven I, Spear AD. Materials
design using genetic algorithms informed by convolutional 73. Sun X, Yue L, Yu L, et al. Machine learning-evolutionary
neural networks: Application to carbon nanotube bundles. algorithm enabled design for 4D-printed active composite
Compos Part B Eng. 2024;286:111751. structures. Adv Funct Mater. 2022;32(10):2109805.

Volume 2 Issue 3 (2025) 26 doi: 10.36922/IJAMD025210016