Page 34 - IJAMD-2-3
P. 34

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



               composites. Compos Struct. 2023;321:117197.        doi: 10.1002/pc.29055
               doi: 10.1016/j.compstruct.2023.117197           106. Daghigh V, Ramezani SB, Daghigh H, Lacy TE Jr. Explainable
                                                                  artificial intelligence prediction of defect characterization in
            94.  Niaki SA, Haghighat E, Campbell T, Poursartip A, Vaziri R.
               Physics-informed neural network for modelling the   composite materials. Compos Sci Technol. 2024;256:110759.
               thermochemical curing process of composite-tool systems      doi: 10.3390/asi7060121
               during  manufacture.  Comput Methods Appl Mech Eng.
               2021;384:113959.                                107. Song Y, Kim K, Park S, Park SK, Park J. Analysis of load-
                                                                  bearing capacity factors of textile-reinforced mortar using
            95.  Yuan L, Li J, Wang B,  et al. Temperature dynamics and   multilayer perceptron and explainable artificial intelligence.
               mechanical properties analysis of carbon fiber epoxy   Construct Build Mater. 2023;363:129560.
               composites radiated by nuclear explosion simulated light
               source. Sci Rep. 2025;15(1):1799.                  doi: 10.1016/j.conbuildmat.2022.129560
                                                               108. Kulasooriya W, Ranasinghe R, Perera US, Thisovithan P,
               doi: 10.1038/s41598-025-85959-3
                                                                  Ekanayake I, Meddage D. Modeling strength characteristics
            96.  Wang S, Sankaran S, Wang H, Perdikaris P.  An Expert’s   of basalt fiber reinforced concrete using multiple explainable
               Guide to Training Physics-Informed Neural Networks. [arXiv   machine  learning  with  a  graphical  user  interface.  Sci
               Preprint]; 2023.                                   Rep. 2023;13(1):13138.
            97.  Fang  Z. A  high-efficient hybrid  physics-informed  neural      doi: 10.1038/s41598-023-40513-x
               networks based on convolutional neural network.  IEEE
               Trans Neural Netw Learn Syst. 2021;33(10):5514-5526.  109. Meister S, Wermes M, Stüve J, Groves RM. Investigations
                                                                  on explainable artificial intelligence methods for the
               doi: 10.1109/TNNLS.2021.3070878                    deep learning classification of fibre layup defect in the
            98.  Nascimento RG, Corbetta M, Kulkarni CS, Viana FA. Hybrid   automated composite manufacturing. Compos Part B Eng.
               physics-informed neural networks for lithium-ion battery   2021;224:109160.
               modeling and prognosis. J Power Sources. 2021;513:230526.     doi: 10.1016/j.compositesb.2021.109160
            99.  Hanna JM, Aguado JV, Comas-Cardona S, Le Guennec Y,   110. Gupta S, Mukhopadhyay T, Kushvaha V. Microstructural
               Borzacchiello D.  A  Self-Supervised Learning Framework   image based convolutional neural networks for efficient
               Based on Physics-Informed and Convolutional Neural   prediction of full-field stress maps in short fiber polymer
               Networks to Identify Local Anisotropic Permeability Tensor   composites. Defence Technol. 2023;24:58-82.
               from Textiles 2D Images for Filling Pattern Prediction.
               Amsterdam: Elsevier; 2024.                         doi: 10.1016/j.dt.2022.09.008
            100. Korolev D, Schmidt T, Natarajan DK, et al. Hybrid Machine   111. Baidoo-Anu D, Ansah LO. Education in the era of generative
               Learning Based Scale Bridging Framework for Permeability   artificial intelligence (AI): Understanding the potential
               Prediction of Fibrous Structures. [arXiv Preprint]; 2025.  benefits of ChatGPT in promoting teaching and learning.
                                                                  J AI. 2023;7(1):52-62.
            101. Gal Y, Islam R, Ghahramani Z. Deep Bayesian Active
               Learning with Image Data. In:  Proceedings of Machine   112. Fui-Hoon Nah F, Zheng R, Cai J, Siau K, Chen L. Generative
               Learning Research; 2017. p. 1183-92.               AI and ChatGPT: Applications, Challenges, and AI-Human
                                                                  Collaboration. United Kingdom: Taylor and Francis; 2023.
            102. Xu F, Uszkoreit H, Du Y, Fan W, Zhao D, Zhu J. Explainable   p. 277-304.
               AI: A Brief Survey on History, Research Areas, Approaches
               and Challenges. Berlin: Springer; 2019. p. 563-574.  113. Mescheder L, Nowozin S, Geiger A. Adversarial Variational
                                                                  Bayes: Unifying Variational Autoencoders and Generative
            103. Dwivedi R, Dave D, Naik H, et al. Explainable AI (XAI):   Adversarial Networks. In: Proceedings of Machine Learning
               Core ideas, techniques, and solutions. ACM Comput Surv.   Research; 2017. p. 2391-2400.
               2023;55(9):1-33.
                                                               114. Mishra A, Krishna Reddy S, Mittal A, Murthy HA.
               doi: 10.1145/3561048                               A  Generative  Model  for  Zero  Shot  Learning  using
            104. Yossef M, Noureldin M, Alqabbany A. Explainable artificial   Conditional Variational Autoencoders. In:  Proceedings
               intelligence framework for FRP composites design. Compos   of the IEEE Conference on Computer Vision and Pattern
               Struct. 2024;341:118190.                           Recognition (CVPR) Workshops; 2018. p. 2188-2196.
               doi: 10.1016/j.compstruct.2024.118190           115. Teimouri A, Li G. Machine Learning-driven discovery
                                                                  of thermoset shape memory polymers with high glass
            105. Azad MM, Kim HS. An explainable artificial intelligence‐
               based  approach  for  reliable  damage  detection  in  polymer   transition temperature using variational autoencoders.
               composite structures using deep learning. Polym Compos.   J Polym Sci. 2025;63:1095-1107.
               2025;46(2):1536-1551.                              doi: 10.1002/pol.20241095


            Volume 2 Issue 3 (2025)                         28                        doi: 10.36922/IJAMD025210016
   29   30   31   32   33   34   35   36   37   38   39