Page 31 - IJAMD-2-3
P. 31
International Journal of AI for
Materials and Design AI applications in composite materials
30. Wang W, Wang H, Zhou J, Fan H, Liu X. Machine 42. Rawat W, Wang Z. Deep convolutional neural networks
learning prediction of mechanical properties of braided- for image classification: A comprehensive review. Neural
textile reinforced tubular structures. Mater Design. Comput. 2017;29(9):2352-2449.
2021;212:110181.
doi: 10.1162/NECO_a_00990
doi: 10.1016/j.matdes.2021.110181
43. Dhillon A, Verma GK. Convolutional neural network:
31. Ding X, Gu Z, Hou X, Xia M, Ismail Y, Ye J. Effects of defects A review of models, methodologies and applications to
on the transverse mechanical response of unidirectional object detection. Prog Artif Intell. 2020;9(2):85-112.
fibre-reinforced polymers: DEM simulation and deep doi: 10.1007/s13748-019-00190-9
learning prediction. Compos Struct. 2023;321:117301.
44. Lawrence S, Giles CL, Tsoi AC, Back AD. Face recognition:
doi: 10.1016/j.compstruct.2023.117301
A convolutional neural-network approach. IEEE Trans
32. Hong H, Kim W, Kim S, Lee K, Kim SS. Deep transfer Neural Netw. 1997;8(1):98-113.
learning for efficient and accurate prediction of composite doi: 10.1109/72.554195
pressure vessel behaviors. Compos Part A Appl Sci Manuf.
2024;186:108413. 45. Hong H, Kim W, Lee K, Kim SS. Prediction of stacking
angles of fiber-reinforced composite materials using deep
doi: 10.1016/j.compositesa.2024.108413 learning based on convolutional neural networks. Compos
33. Zhang Z, Zhou H, Ma J, et al. Space deployable bistable Res. 2023;36(1):48-52.
composite structures with C-cross section based on machine 46. Caglar B, Broggi G, Ali MA, Orgéas L, Michaud V. Deep
learning and multi-objective optimization. Compos Struct. learning accelerated prediction of the permeability of
2022;297:115983. fibrous microstructures. Compos Part A Appl Sci Manuf.
doi: 10.1016/j.compstruct.2022.115983 2022;158:106973.
34. Tao F, Liu X, Du H, Yu W. Learning composite constitutive doi: 10.1016/j.compositesa.2022.106973
laws via coupling Abaqus and deep neural network. Compos 47. Kojima Y, Hirayama K, Endo K, Harada Y, Muramatsu M.
Struct. 2021;272:114137. Transfer-learning-aided defect prediction in simply shaped
doi: 10.1016/j.compstruct.2021.114137 CFRP specimens based on stress distribution obtained from
finite element analysis and infrared stress measurement.
35. Schmidt T, Natarajan DK, Duhovic M, Cassola S, Nuske M, Compos Part B Eng. 2025;291:111958.
May D. Numerical data generation for building machine
learning models for permeability estimation of fibrous doi: 10.1016/j.compositesb.2024.111958
structures. Polym Compos. 2025:1-17. 48. Guild F, Summerscales J. Microstructural image analysis
doi: 10.1002/pc.29768 applied to fibre composite materials: A review. Composites.
1993;24(5):383-393.
36. Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C. A Survey on
Deep Transfer Learning. Berlin: Springer; 2018. p. 270-279. doi: 10.1016/0010-4361(93)90246-5
37. Long M, Zhu H, Wang J, Jordan MI. Deep Transfer Learning 49. D’orazio T, Leo M, Distante A, Guaragnella C, Pianese V,
with Joint Adaptation Networks. In: Proceedings of Machine Cavaccini G. Automatic ultrasonic inspection for internal
Learning Research; 2017. p. 2208-2217. defect detection in composite materials. NDT E Int.
2008;41(2):145-154.
38. Zhuang F, Qi Z, Duan K, et al. A comprehensive survey on
transfer learning. Proceed IEEE. 2020;109(1):43-76. doi: 10.1016/j.ndteint.2007.08.001
doi: 10.1109/JPROC.2019.2955636 50. Bhaduri A, Gupta A, Graham-Brady L. Stress field prediction
in fiber-reinforced composite materials using a deep learning
39. O’shea K, Nash R. An Introduction to Convolutional Neural approach. Compos Part B Eng. 2022;238:109879.
Networks. [arXiv Preprint]; 2015.
doi: 10.1016/j.compositesb.2022.109879
doi: 10.48550/arXiv.1511.08458
51. Kim DW, Lim JH, Lee S. Prediction and validation of the
40. Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional transverse mechanical behavior of unidirectional composites
neural networks: An overview and application in radiology. considering interfacial debonding through convolutional
Insights Imaging. 2018;9:611-629.
neural networks. Compos Part B Eng. 2021;225:109314.
doi: 10.1007/s13244-018-0639-9
doi: 10.1016/j.compstruct.2020.109314
41. Gu J, Wang Z, Kuen J, et al. Recent advances in convolutional 52. Abueidda DW, Almasri M, Ammourah R, Ravaioli U,
neural networks. Pattern Recognit. 2018;77:354-377.
Jasiuk IM, Sobh NA. Prediction and optimization of
doi: 10.1016/j.patcog.2017.10.013 mechanical properties of composites using convolutional
Volume 2 Issue 3 (2025) 25 doi: 10.36922/IJAMD025210016

