Industrial Applications of AI in Aircraft Manufacturing: A PRISMA Systematic Literature Review

Pierrick Bougault; Raphael El Haddad; Liang Ma

doi:10.30657/pea.2026.32.5

References

Abbas, M, Nazir, A, Ali, U, 2025. Design, additive manufacturing, and machine learning prediction of multi-material diamond tpms structure for improved mechanical performance. Progress in Additive Manufacturing, 10, 9793–9809, DOI: 10.1007/s40964-025-01209-y.
Search in Google Scholar Back to article
Abdulla, A, Baryannis, G, 2024. A hybrid multi-criteria decision-making and machine learning approach for explainable supplier selection. Supply Chain Analytics, 7, 100074, DOI: 10.1016/j.sca.2024.100074.
Search in Google Scholar Back to article
Abdullah, R, Tze, P, C, Hafizh, H, Abdul Rasib, A, H, Mansour, M, 2025. Part detection model for aerospace manufacturing quality control using convolutional neural networks. Journal of Advanced Research Design, 133(1), 1–10, DOI: 10.37934/ard.133.1.110.
Search in Google Scholar Back to article
Abdulrahman, Y, Parezanovic, V, Svetinovic, D, 2022. AI-blockchain systems in aerospace engineering and management: review and challenges. 2022 30th Telecommunications Forum (TELFOR), 1–4, DOI: 10.1109/TELFOR56187.2022.9983700.
Search in Google Scholar Back to article
Abidi, M, H, Mohammed, M, K, Alkhalefah, H, 2022. Predictive maintenance planning for industry 4.0 using machine learning for sustainable manufacturing. Sustainability, 14(6), 3387, DOI: 10.3390/su14063387.
Search in Google Scholar Back to article
Adeniji, D, Schoop, J, 2022. In-situ calibrated digital process twin models for resource efficient manufacturing. Journal of Manufacturing Science and Engineering, 144(4), 041008, DOI: 10.1115/1.4052131.
Search in Google Scholar Back to article
Adizue, U, L, Tura, A, D, Isaya, E, O, Farkas, B, Z, Takács, M, 2023. Surface quality prediction by machine learning methods and process parameter optimization in ultra-precision machining of aisi d2 using cbn tool. The International Journal of Advanced Manufacturing Technology, 129(3–4), 1375–1394, DOI: 10.1007/s00170-023-12366-1.
Search in Google Scholar Back to article
Aggarwal, P, Narwal, B, Mohapatra, A, K, Bhati, P, 2023. Digital twin in the aviation industry and the role of machine learning: a review. ICT Analysis and Applications, 782, 445–455, DOI: 10.1007/978-981-99-6568-7_41.
Search in Google Scholar Back to article
Agustian, E, S, Pratama, Z, A, 2024. Artificial intelligence application on aircraft maintenance: a systematic literature review. EAI Endorsed Transactions on Internet of Things, 10, DOI: 10.4108/eetiot.6938.
Search in Google Scholar Back to article
Ahfock, D, McLachlan, G, J, 2023. Semi-supervised learning of classifiers from a statistical perspective: a brief review. Econometrics and Statistics, 26, 124–138, DOI: 10.1016/j.ecosta.2022.03.007.
Search in Google Scholar Back to article
Aicher, W, Branger, J, Van Dijk, G, Ertelt, J, Hück, M, De Jonge, J, Lowak, H, Rhomberg, H, Schütz, D, Schütz, W, 1976. Description of a fighter aircraft loading standard for fatigue evaluation falstaff. Common Report of FCW Emmen, LBF, NRL, IABG.
Search in Google Scholar Back to article
Akhavan, J, Xu, K, Vallabh, C, K, Manoochehri, S, 2024. Real-time print tracking in metal additive manufacturing using acoustic emission sensors and vision transformer algorithms. MSEC2024, 10, DOI: 10.1115/MSEC2024-125391.
Search in Google Scholar Back to article
Akhbardeh, F, Desell, T, Zampieri, M, 2020. MaintNet: a collaborative open-source library for predictive maintenance language resources. Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations, 7–11, DOI: 10.18653/v1/2020.coling-demos.2.
Search in Google Scholar Back to article
Akhil, V, Raghav, G, Arunachalam, N, Srinivas, D, S, 2020. Image databased surface texture characterization and prediction using machine learning approaches for additive manufacturing. Journal of Computing and Information Science in Engineering, 20(2), 021010, DOI: 10.1115/1.4045719.
Search in Google Scholar Back to article
Al-Haddad, L, A, Mahdi, N, M, 2024. Efficient multidisciplinary modeling of aircraft undercarriage landing gear using data-driven naïve bayes and finite element analysis. Multiscale and Multidisciplinary Modeling, Experiments and Design, 7, 3187–3199, DOI: 10.1007/s41939-024-00389-4.
Search in Google Scholar Back to article
Alharbi, F, Luo, S, Zhao, S, Yang, G, Wheeler, C, Chen, Z, 2024. Belt conveyor idlers fault detection using acoustic analysis and deep learning algorithm with the yamnet pretrained network. IEEE Sensors Journal, 24(19), 31379–31394, DOI: 10.1109/JSEN.2024.3439509.
Search in Google Scholar Back to article
Ali, M, Sajjad, U, Hussain, I, Abbas, N, Ali, H, M, Yan, W,-M, Wang, C,-C, 2022. On the assessment of the mechanical properties of additively manufactured lattice structures. Engineering Analysis with Boundary Elements, 142, 93–116, DOI: 10.1016/j.enganabound.2022.05.019.
Search in Google Scholar Back to article
Almuqati, M, T, Sidi, F, Rum, S, N, M, Zolkepli, M, Ishak, I, 2024. Challenges in supervised and unsupervised learning: a comprehensive overview. International Journal on Advanced Science, Engineering and Information Technology, 14(4), 1449–1455, DOI: 10.18517/ijaseit.14.4.20191.
Search in Google Scholar Back to article
Alogla, A, A, Alruqi, M, 2021. Aircraft assembly snags: human errors or lack of production design? Aerospace, 8(12), 391, DOI: 10.3390/aerospace8120391.
Search in Google Scholar Back to article
Alreshidi, I, Moulitsas, I, Jenkins, K, W, 2024. Advancing aviation safety through machine learning and psychophysiological data: a systematic review. IEEE Access, 12, 5132–5150, DOI: 10.1109/ACCESS.2024.3349495.
Search in Google Scholar Back to article
Amini, A, Gan, T,-H, 2022. A machine learning based model for monitoring of composites’ drilling-induced defects during assembly production using terahertz imaging data. 2022 IEEE Workshop on Complexity in Engineering (COMPENG), 1–5, DOI: 10.1109/COMPENG50184.2022.9905438.
Search in Google Scholar Back to article
Arora, R, Bhatia, V, Sidharth, S, 2024. Military aircraft surface defect detection using machine learning. 2024 International Conference on Computing, Sciences and Communications (ICCSC), 1–6, DOI: 10.1109/ICCSC62048.2024.10830372.
Search in Google Scholar Back to article
Auyeskhan, U, Turysbekov, G, Roshaven, S, P, 2025. Decision-making framework supported by techno-economic analysis of laser powder bed fusion: a novel approach using retrieved augmentation generation (rag). Progress in Additive Manufacturing, 10, 9099–9115, DOI: 10.1007/s40964-025-01173-7.
Search in Google Scholar Back to article
Awd, M, Walther, F, 2025. AI-powered very-high-cycle fatigue control: optimizing microstructural design for selective laser melted ti-6al-4v. Materials, 18(7), DOI: 10.3390/ma18071472.
Search in Google Scholar Back to article
Azar, K, Hajiakhondi-Meybodi, Z, Naderkhani, F, 2022. Semi-supervised clustering-based method for fault diagnosis and prognosis: a case study. Reliability Engineering & System Safety, 222, 108405, DOI: 10.1016/j.ress.2022.108405.
Search in Google Scholar Back to article
Azar, K, Naderkhani, F, 2020. Semi-supervised learning approach for optimizing condition-based-maintenance (cbm) decisions. 2020 IEEE International Conference on Prognostics and Health Management (ICPHM), 1–6, DOI: 10.1109/ICPHM49022.2020.9187022.
Search in Google Scholar Back to article
B. Bala S. Behal 2024. A brief survey of data preprocessing in machine learning and deep learning techniques. 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 1755–1762, DOI: 10.1109/I-SMAC61858.2024.10714767.
Search in Google Scholar Back to article
Bacciaglia, A, Ciccone, F, Ceruti, A, Peruzzini, M, 2025a. 2D frequencybased topological optimization: machine learning to aid tuning simulation parameters. Design Tools and Methods in Industrial Engineering IV, 20–29, DOI: 10.1007/978-3-031-76597-1_3.
Search in Google Scholar Back to article
Bacciaglia, A, Ciccone, F, Ceruti, A, Peruzzini, M, 2025b. 2D frequencybased topological optimization: efficient dataset creation for neural networks to aid in tuning simulation parameters. International Journal on Interactive Design and Manufacturing (IJIDeM), DOI: 10.1007/s12008-025-02349-9.
Search in Google Scholar Back to article
Baidzawi, I, J, Nur, N, M, Shukri, S, A, Aris, K, D, M, Asmadi, N, L, Mohamed, M, 2019. A review of accidents & incidents on boeing and airbus commercial aircraft’s avionics-related system in two decades (1996-2015). IOP Conference Series: Materials Science and Engineering, 697(1), 012031, DOI: 10.1088/1757-899X/697/1/012031.
Search in Google Scholar Back to article
Banabilah, S, Aloqaily, M, Alsayed, E, Malik, N, Jararweh, Y, 2022. Federated learning review: fundamentals, enabling technologies, and future applications. Information Processing & Management, 59(6), 103061, DOI: 10.1016/j.ipm.2022.103061.
Search in Google Scholar Back to article
Barbosa, F, C, 2020. Aircraft engine technology review-the pathways for an efficient, cleaner and quieter aviation industry. SAE Technical Paper, 18, DOI: 10.4271/2019-36-0175.
Search in Google Scholar Back to article
Bautista-Hernández, J, Martín-Prats, M, Á, 2024. Artificial intelligence approach in aerospace for error mitigation. Aerospace, 11(4), 300, DOI: 10.3390/aerospace11040300.
Search in Google Scholar Back to article
Bergen, S, Huso, M, M, Duerr, A, E, Braham, M, A, Schmuecker, S, Miller, T, A, Katzner, T, E, 2023. A review of supervised learning methods for classifying animal behavioural states from environmental features. Methods in Ecology and Evolution, 14(1), 189–202, DOI: 10.1111/2041-210X.14019.
Search in Google Scholar Back to article
Bertolini, M, Mezzogori, D, Neroni, M, Zammori, F, 2021. Machine learning for industrial applications: a comprehensive literature review. Expert Systems with Applications, 175, 114820, DOI: 10.1016/j.eswa.2021.114820.
Search in Google Scholar Back to article
Bharat, N, Kumar, V, Mishra, V, Veeman, D, Vellaisamy, M, 2025. Influence of 3d printing fdm process parameters on compressive strength of pla/carbon fiber composites: anova and backpropagation neural network approach. Journal of Materials Engineering and Performance, 34, 20830–20843, DOI: 10.1007/s11665-025-10783-9.
Search in Google Scholar Back to article
Birhane, A, Kasirzadeh, A, Leslie, D, Wachter, S, 2023. Science in the age of large language models. Nature Reviews Physics, 5(5), 277–280, DOI: 10.1038/s42254-023-00581-4.
Search in Google Scholar Back to article
Brunton, S, L, Nathan Kutz, J, Manohar, K, Aravkin, A, Y, Morgansen, K, Klemisch, J, Goebel, N, Buttrick, J, Poskin, J, Blom-Schieber, A, W, Hogan, T, McDonald, D, 2021. Data-driven aerospace engineering: reframing the industry with machine learning. AIAA Journal, 59(8), 1–26, DOI: 10.2514/1.J060131.
Search in Google Scholar Back to article
Burnham, J, F, 2006. Scopus database: a review. Biomedical digital libraries, 3(1), 1–8, DOI: 10.1186/1742-5581-3-1.
Search in Google Scholar Back to article
Caiazzo, F, Caggiano, A, 2018. Laser direct metal deposition of 2024 al alloy: trace geometry prediction via machine learning. Materials, 11(3), 444, DOI: 10.3390/ma11030444.
Search in Google Scholar Back to article
Cao, Q, Zanni-Merk, C, Reich, C, 2019. Ontologies for manufacturing process modeling: a survey. Sustainable Design and Manufacturing 2018, 130, 61–70, DOI: 10.1007/978-3-030-04290-5_7.
Search in Google Scholar Back to article
Castro, P, Pathinettampadian, G, Ravi, C, S, D, Subramaniyan, M, K, 2023. Prediction of compressive strength in additively fabricated part using long short term memory based neural network. Materials Today Communications, 37, 107139, DOI: 10.1016/j.mtcomm.2023.107139.
Search in Google Scholar Back to article
Chan, P, A, Kumar, D, Liu, Y, Namilae, S, Defect detection during composite processing using machine learning methods. AIAA SCITECH 2025 Forum, DOI: 10.2514/6.2025-0824.
Search in Google Scholar Back to article
Chandrasekaran, B, 2025. Enhancing efficiency in manufacturing through automated ai agent-based bearing defect diagnostic system. SAE Technical Paper, 10, DOI: 10.4271/2025-01-0158.
Search in Google Scholar Back to article
Chatterjee, B, Bhowmik, S, 2019. Evolution of material selection in commercial aviation industry—a review. Sustainable Engineering Products and Manufacturing Technologies, 199–219, DOI: 10.1016/B978-0-12-816564-5.00009-8.
Search in Google Scholar Back to article
Chaudhry, M, Shafi, I, Mahnoor, M, Vargas, D, L, R, Thompson, E, B, Ashraf, I, 2023. A systematic literature review on identifying patterns using unsupervised clustering algorithms: a data mining perspective. Symmetry, 15(9), 1679, DOI: 10.3390/sym15091679.
Search in Google Scholar Back to article
Chen, H,-M, Zhang, J,-H, Wang, Y,-C, Chang, H,-C, King, J,-K, Yang, C,- T, 2023a. Hot-pressing furnace current monitoring and predictive maintenance system in aerospace applications. Sensors, 23(4), 2230, DOI: 10.3390/s23042230.
Search in Google Scholar Back to article
Chen, J, Qi, G, Wang, K, 2023b. Synergizing machine learning and the aviation sector in lithium-ion battery applications: a review. Energies, 16(17), 6318, DOI: 10.3390/en16176318.
Search in Google Scholar Back to article
Chen, Y, Yan, Z, Zhu, Y, 2024. A comprehensive survey for generative data augmentation. Neurocomputing, 600, 128167, DOI: 10.1016/j.neucom.2024.128167.
Search in Google Scholar Back to article
Chibane, H, Dubois, S, Guio, R, D, 2021. Innovation beyond optimization: application to cutting tool design. Computers & Industrial Engineering, 154, 107139, DOI: 10.1016/j.cie.2021.107139.
Search in Google Scholar Back to article
Chinchanikar, S, Shaikh, A, A, 2022. A review on machine learning, big data analytics, and design for additive manufacturing for aerospace applications. Journal of Materials Engineering and Performance, 31(8), 6112–6130, DOI: 10.1007/s11665-022-07125-4.
Search in Google Scholar Back to article
Choi, J,-H, Yang, J, Jang, J, Pang, H, Cho, J,-M, Yu, R, 2023. Machine learning-assisted two-step homogenization framework of short fiberreinforced plastics. ICCM 2023.
Search in Google Scholar Back to article
Choudhary, T, Bhargava, L, 2025. Artificial neural network based model for single event upset current prediction in 14 nm finfet. 2025 Fourth International Conference on Smart Technologies, Communication and Robotics (STCR), 1–7, DOI: 10.1109/STCR62650.2025.11019066.
Search in Google Scholar Back to article
Ciano, M, P, Peron, M, Panza, L, Pozzi, R, 2025. Industry 4.0 technologies in support of circular economy: a 10r-based integration framework. Computers & Industrial Engineering, 201, 110867, DOI: 10.1016/j.cie.2025.110867.
Search in Google Scholar Back to article
Crawford, B, Sourki, R, Khayyam, H, S. Milani, A, 2021. A machine learning framework with dataset-knowledgeability pre-assessment and a local decision-boundary crispness score: an industry 4.0-based case study on composite autoclave manufacturing. Computers in Industry, 132, 103510, DOI: 10.1016/j.compind.2021.103510.
Search in Google Scholar Back to article
Das, M, Sahu, S, Parhi, D, R, 2020. A review of application of composite materials for aerospace structures and its damage detection using artificial intelligence techniques. International Conference on Artificial Intelligence in Manufacturing & Renewable Energy (ICAIMRE) 2019, DOI: 10.2139/ssrn.3714181.
Search in Google Scholar Back to article
Das, M, Sahu, S, Parhi, D, 2021. Composite materials and their damage detection using ai techniques for aerospace application: a brief review. Materials Today: Proceedings, 44, 955–960, DOI: 10.1016/j.matpr.2020.11.005.
Search in Google Scholar Back to article
Dasari, S, K, Cheddad, A, Palmquist, J, 2020. Melt-pool defects classification for additive manufactured components in aerospace usecase. 2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI), 249–254, DOI: 10.1109/ISCMI51676.2020.9311555.
Search in Google Scholar Back to article
De Oliveira Morais, L, Krus, P, Pereira, L, 2024. A human-centered systems engineering approach for integrating artificial intelligence in aviation: a review of ai systems. 34th Congress of the International Council of the Aeronautical Sciences.
Search in Google Scholar Back to article
De Simone, V, Pasquale, V, D, Miranda, S, 2023. An overview on the use of ai/ml in manufacturing msmes: solved issues, limits, and challenges. Procedia Computer Science, 217, 1820–1829, DOI: 10.1016/j.procs.2022.12.382.
Search in Google Scholar Back to article
Dellermann, D, Ebel, P, Söllner, M, Leimeister, J, M, 2019. Hybrid intelligence. Business & Information Systems Engineering, 61(5), 637–643, DOI: 10.1007/s12599-019-00595-2.
Search in Google Scholar Back to article
Dhamija, P, Bag, S, 2020. Role of artificial intelligence in operations environment: a review and bibliometric analysis. The TQM Journal, 32(4), 869–896, DOI: 10.1108/TQM-10-2019-0243.
Search in Google Scholar Back to article
Dhrangdhariya, P, Saini, P, Maiti, S, Rai, B, 2025. Multi-class classification of paint/coating defects using transfer learning. Engineering Applications of Artificial Intelligence, 156, 111320, DOI: https://doi.org/10.1016/j.engappai.2025.111320.
Search in Google Scholar Back to article
Dilberoglu, U, M, Gharehpapagh, B, Yaman, U, Dolen, M, 2017. The role of additive manufacturing in the era of industry 4.0. Procedia Manufacturing, 11, 545–554, DOI: 10.1016/j.promfg.2017.07.148.
Search in Google Scholar Back to article
Dinneweth, J, Boubezoul, A, Mandiau, R, Espié, S, 2022. Multi-agent reinforcement learning for autonomous vehicles: a survey. Autonomous Intelligent Systems, 2(1), 27, DOI: 10.1007/s43684-022-00045-z.
Search in Google Scholar Back to article
Dolganova, L, Bach, V, Rödl, A, Kaltschmitt, M, Finkbeiner, M, 2022. Assessment of critical resource use in aircraft manufacturing. Circular economy and sustainability, 2(3), 1193–1212, DOI: 10.1007/s43615-022-00157-x.
Search in Google Scholar Back to article
Doodi, R, Gunji, B, M, 2023. Prediction and experimental validation approach to improve performance of novel hybrid bio-inspired 3d printed lattice structures using artificial neural networks. Scientific Reports, 13(1), 7763, DOI: 10.1038/s41598-023-33935-0.
Search in Google Scholar Back to article
Dou, Z, Peng, Z, 2023. Intelligent tool cabinet system based on yolo object detection algorithm. 2023 International Conference on Artificial Intelligence and Automation Control (AIAC), 17–20, DOI: 10.1109/AIAC61660.2023.00036.
Search in Google Scholar Back to article
Duarte, J, M, Berton, L, 2023. A review of semi-supervised learning for text classification. Artificial Intelligence Review, 56(9), 9401–9469, DOI: 10.1007/s10462-023-10393-8.
Search in Google Scholar Back to article
Dubey, A, Rahman Ayubee, A, Kumar Singh, A, Singh, C, Ranjan, J, Sahu, S, Kaur, M, 2023. A review: most important and frequently used algorithms in supervised learning. Proceedings of the KILBY 100 7th International Conference on Computing Sciences 2023 (ICCS 2023), DOI: 10.2139/ssrn.4482730.
Search in Google Scholar Back to article
Eckley, Z, Murphy, M, Paudyal, R, Poudel, K, Cankaya, B, 2025. Designing an aerodynamic airfoil with machine learning; the utilization of generative adversarial networks for the production of airfoil geometries. AIAA SCITECH 2025 Forum, DOI: 10.2514/6.2025-0703.
Search in Google Scholar Back to article
Eddy, D, White, M, Blanchette, D, 2023a. Learned manufacturing inspection inferences from image recognition capabilities. Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus, 193–202, DOI: 10.1007/978-3-031-17629-6_21.
Search in Google Scholar Back to article
Eddy, D, White, M, Blanchette, D, 2023b. Intelligent insights for manufacturing inspections from efficient image recognition. Machines, 11(1), 45, DOI: 10.3390/machines11010045.
Search in Google Scholar Back to article
Ernst, A, Weigold, M, 2021. MACHINE data-based prediction of blisk blade geometry characteristics. MM Science Journal, 2021(5), 5046–5051, DOI: 10.17973/MMSJ.2021_11_2021151.
Search in Google Scholar Back to article
Etem, T, 2025. Advancing non-destructive weld assessment using autoencoder-based feature extraction. Nondestructive Testing and Evaluation, 40(10), 4598–4618, DOI: 10.1080/10589759.2025.2500551.
Search in Google Scholar Back to article
Fabris, F, Magalhães, J, P, D, Freitas, A, A, 2017. A review of supervised machine learning applied to ageing research. Biogerontology, 18(2), 171–188, DOI: 10.1007/s10522-017-9683-y.
Search in Google Scholar Back to article
Farahani, A, Pourshojae, B, Rasheed, K, Arabnia, H, R, 2020. A concise review of transfer learning. 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 344–351, DOI: 10.1109/CSCI51800.2020.00065.
Search in Google Scholar Back to article
Fernandes, H, Zhang, H, Figueiredo, A, Malheiros, F, Ignacio, L, Sfarra, S, Ibarra-Castanedo, C, Guimaraes, G, Maldague, X, 2018. Machine learning and infrared thermography for fiber orientation assessment on randomly-oriented strands parts. Sensors, 18(1), 288, DOI: 10.3390/s18010288.
Search in Google Scholar Back to article
Filz, M,-A, Langner, J, E, B, Herrmann, C, Thiede, S, 2021. Data-driven failure mode and effect analysis (fmea) to enhance maintenance planning. Computers in Industry, 129, 103451, DOI: 10.1016/j.compind.2021.103451.
Search in Google Scholar Back to article
Fotovvati, B, Chou, K, 2022. Build surface study of single-layer raster scanning in selective laser melting: surface roughness prediction using deep learning. Manufacturing Letters, 33, 701–711, DOI: 10.1016/j.mfglet.2022.07.088.
Search in Google Scholar Back to article
Frederick, D, K, DeCastro, J, A, Litt, J, S, 2007. User’s guide for the commercial modular aero-propulsion system simulation (c-mapss). NASA.
Search in Google Scholar Back to article
Frey Marioni, Y, Adami, P, Vazquez Diaz, R, Cassinelli, A, Sherwin, S, Montomoli, F, 2022. Development of machine-learnt turbulence closures for wake mixing predictions in low-pressure turbines. , Volume 10C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V10CT32A032, DOI: 10.1115/GT2022-82531.
Search in Google Scholar Back to article
Fysikopoulos, A, Alexopoulos, T, Pastras, G, Stavropoulos, P, Chryssolouris, G, 2015. On the design of a sustainable production line: the metacam tool. , Volume 15: Advances in Multidisciplinary Engineering, V015T19A015, DOI: 10.1115/IMECE2015-52960.
Search in Google Scholar Back to article
Gain, S, Veeman, D, Vellaisamy, M, 2025. Investigation of process parameters of inconel 625 fabricated parts by wire arc additive manufacturing. Journal of Materials Engineering and Performance, DOI: 10.1007/s11665-025-11503-z.
Search in Google Scholar Back to article
Gao, Z, Mavris, D, N, 2022. Statistics and machine learning in aviation environmental impact analysis: a survey of recent progress. Aerospace, 9(12), 750, DOI: 10.3390/aerospace9120750.
Search in Google Scholar Back to article
Garcia, A, B, Babiceanu, R, F, Seker, R, 2021. Artificial intelligence and machine learning approaches for aviation cybersecurity: an overview. 2021 integrated communications navigation and surveillance conference (ICNS), 1–8, DOI: 10.1109/ICNS52807.2021.9441594.
Search in Google Scholar Back to article
Garcia-Perez, A, Gomez-Silva, M, J, De La Escalera-Hueso, A, 2024. A generative ai approach to improve in-situ vision tool wear monitoring with scarce data. Journal of Intelligent Manufacturing, 36, 3165–3183, DOI: 10.1007/s10845-024-02379-2.
Search in Google Scholar Back to article
Gohardani, A, S, Doulgeris, G, Singh, R, 2011. Challenges of future aircraft propulsion: a review of distributed propulsion technology and its potential application for the all electric commercial aircraft. Progress in Aerospace Sciences, 47(5), 369–391, DOI: 10.1016/j.paerosci.2010.09.001.
Search in Google Scholar Back to article
Grozav, S, D, Sterca, A, D, Kočiško, M, Pollák, M, Ceclan, V, 2023. Artificial neural network-based predictive model for finite element analysis of additive-manufactured components. Machines, 11(5), 547, DOI: 10.3390/machines11050547.
Search in Google Scholar Back to article
Gzar, D, A, Mahmood, A, M, Abbas, M, K, 2022. A comparative study of regression machine learning algorithms: tradeoff between accuracy and computational complexity. Mathematical Modelling of Engineering Problems, 9(5), 1217–1224, DOI: 10.18280/mmep.090508.
Search in Google Scholar Back to article
Halevi, G, Moed, H, Bar-Ilan, J, 2017. Suitability of google scholar as a source of scientific information and as a source of data for scientific evaluation—review of the literature. Journal of Informetrics, 11(3), 823–834, DOI: 10.1016/j.joi.2017.06.005.
Search in Google Scholar Back to article
Han, K, Sheng, V, S, Song, Y, Liu, Y, Qiu, C, Ma, S, Liu, Z, 2024. Deep semi-supervised learning for medical image segmentation: a review. Expert Systems with Applications, 245, 123052, DOI: 10.1016/j.eswa.2023.123052.
Search in Google Scholar Back to article
Hasib, A, A, Rahman, A, Khabir, M, Shawon, M, T, R, 2023. An interpretable systematic review of machine learning models for predictive maintenance of aircraft engine. arXiv preprint arXiv:2309.13310, DOI: 10.48550/arXiv.2309.13310.
Search in Google Scholar Back to article
Hassan, F, Khan, A, H, 2025. Predicting actual temperature of an autoclave for composite materials using balanced-elasticnet. Transportation Research Procedia, 84, 193–200, DOI: 10.1016/j.trpro.2025.03.063.
Search in Google Scholar Back to article
Hassan, K, Thakur, A, K, Singh, G, Singh, J, Gupta, L, R, Singh, R, 2024. Application of artificial intelligence in aerospace engineering and its future directions: a systematic quantitative literature review. Archives of Computational Methods in Engineering, 31, 4031–4086, DOI: 10.1007/s11831-024-10105-7.
Search in Google Scholar Back to article
He, X, Wang, N, Zhou, Q, Huang, J, Ramakrishna, S, Li, F, 2024. Smart aviation biofuel energy system coupling with machine learning technology. Renewable and Sustainable Energy Reviews, 189, 113914, DOI: 10.1016/j.rser.2023.113914.
Search in Google Scholar Back to article
Hemmer, P, Vössing, M, Schemmer, M, Kühl, N, 2021. Human-ai complementarity in hybrid intelligence systems: a structured literature review. Proceedings of the 25th Pacific Asia Conference on Information Systems, 78, 118.
Search in Google Scholar Back to article
Herrera, A, R, Alvarez, J, Restrepo, J, Herrera, C, Rodríguez, S, Escobar, C, A, Vásquez, R, E, Sierra-Pérez, J, 2024. Damage detection and localization methodology based on strain measurements and finite element analysis: structural health monitoring in the context of industry 4.0. Aerospace, 11(9), 708, DOI: 10.3390/aerospace11090708.
Search in Google Scholar Back to article
Higgins, C, McGarry, L, Butterfield, J, Murphy, A, 2023. Machine learning methods to improve the accuracy of industrial robots. SAE International Journal of Advances and Current Practices in Mobility, 5(5), 1900–1918, DOI: 10.4271/2023-01-1000.
Search in Google Scholar Back to article
Hojjati, A, Adhikari, A, Struckmann, K, Chou, E, Tho Nguyen, T, N, Madan, K, Winslett, M, S, Gunter, C, A, King, W, P, 2016. Leave your phone at the door: side channels that reveal factory floor secrets. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 883–894, DOI: 10.1145/2976749.2978323.
Search in Google Scholar Back to article
Hosna, A, Merry, E, Gyalmo, J, Alom, Z, Aung, Z, Azim, M, A, 2022. Transfer learning: a friendly introduction. Journal of Big Data, 9(1), 102, DOI: 10.1186/s40537-022-00652-w.
Search in Google Scholar Back to article
Hsu, M,-K, Chen, W, Huang, B,-Y, Shen, L,-H, Hsu, C,-H, Chang, R,-Y, Yu, C,-H, 2023. A deep learning empowered smart representative volume element method for long fiber woven composites. Frontiers in Materials, 10, 1179710, DOI: 10.3389/fmats.2023.1179710.
Search in Google Scholar Back to article
Hu, J, Zhao, G, Xiao, W, Li, R, 2023. AR-based deep learning for real-time inspection of cable brackets in aircraft. Robotics and Computer- Integrated Manufacturing, 83, 102574, DOI: 10.1016/j.rcim.2023.102574.
Search in Google Scholar Back to article
Hu, X, Chen, X, Parks, G, T, Yao, W, 2016. Review of improved monte carlo methods in uncertainty-based design optimization for aerospace vehicles. Progress in Aerospace Sciences, 86, 20–27, DOI: 10.1016/j.paerosci.2016.07.004.
Search in Google Scholar Back to article
Hua, T, K, 2022. A short review on machine learning. Authorea Preprints, DOI: 10.22541/au.166490976.66390273/v1.
Search in Google Scholar Back to article
Huang, H, Hadigheh, S, A, Aghabalaei Baghaei, K, 2023. Influences of fibre shape on the transverse modulus of unidirectional fibre reinforced composites using finite element and machine learning methods. Composite Structures, 312, 116872, DOI: 10.1016/j.compstruct.2023.116872.
Search in Google Scholar Back to article
Isaza, C, Guerrero-Garcia, F, Anaya, K, Jenab, K, Ortega-Moody, J, 2024a. A vision-based neural networks model for turbine trench-filler diagnosis. International Congress and Workshop on Industrial AI and eMaintenance 2023, 1–14, DOI: 10.1007/978-3-031-39619-9_1.
Search in Google Scholar Back to article
Isaza, C, Guerrero-Garcia, F, Anaya, K, Jenab, K, Ortega-Moody, J, 2024b. CFM56 turbine trench-filler inspection using instance segmentation. International Journal of System Assurance Engineering and Management, DOI: 10.1007/s13198-024-02424-6.
Search in Google Scholar Back to article
Jan, Z, Ahamed, F, Mayer, W, Patel, N, Grossmann, G, Stumptner, M, Kuusk, A, 2023. Artificial intelligence for industry 4.0: systematic review of applications, challenges, and opportunities. Expert Systems with Applications, 216, 119456, DOI: 10.1016/j.eswa.2022.119456.
Search in Google Scholar Back to article
Jaw, L, C, Lee, Y,-J, 2014. Engine diagnostics in the eyes of machine learning. Proceedings of the ASME turbo expo, 6.
Search in Google Scholar Back to article
Jawalkar, C, Kant, S, others 2015. A review on use of aluminium alloys in aircraft components. i-manager’s Journal on Material Science, 3(3), 33–38, DOI: 10.26634/jms.3.3.3673.
Search in Google Scholar Back to article
Jeung, S,-Y, Kwon, J,-W, 2025. A robust multivariate time series classification approach based on topological data analysis for channel fault tolerance. Sensors, 25(9), DOI: 10.3390/s25092709.
Search in Google Scholar Back to article
Jiang, Y, Tran, T, H, Williams, L, 2023. Machine learning and mixed reality for smart aviation: applications and challenges. Journal of Air Transport Management, 111, 102437, DOI: 10.1016/j.jairtraman.2023.102437.
Search in Google Scholar Back to article
Jiao, R, Zhang, Y, Ding, L, Xue, B, Zhang, J, Cai, R, Jin, C, 2024. Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation. Computers in Biology and Medicine, 169, 107840, DOI: 10.1016/j.compbiomed.2023.107840.
Search in Google Scholar Back to article
Jwo, J,-S, Hsieh, H,-Y, Lee, C,-H, Lin, C,-S, Wang, P,-W, Hong, C,-Y, King, J,-K, Hsu, H,-C, 2022. Simulation and modeling of a data twin service for the autoclave curing process. IEEE Access, 10, 111879–111887, DOI: 10.1109/ACCESS.2022.3216062.
Search in Google Scholar Back to article
Kadir, B, A, Broberg, O, Conceição, C, S, da 2019. Current research and future perspectives on human factors and ergonomics in industry 4.0. Computers & Industrial Engineering, 137, 106004, DOI: 10.1016/j.cie.2019.106004.
Search in Google Scholar Back to article
Karaoğlu, U, Mbah, O, Zeeshan, Q, 2023. Applications of machine learning in aircraft maintenance. J. Eng. Manag. Syst. Eng, 2(1), 76–95, DOI: 10.56578/jemse020105.
Search in Google Scholar Back to article
Kasar, M, M, Bhattacharyya, D, Kim, T, 2016. Face recognition using neural network: a review. International Journal of Security and Its Applications, 10(3), 81–100, DOI: 10.14257/ijsia.2016.10.3.08.
Search in Google Scholar Back to article
Khan, L, Elshennawy, A, Furterer, S, Cudney, E, 2024. MACHINE learning (ml) in aerospace and defense (a&d) industries: a systematic literature review. Journal of Management & Engineering Integration, 17(2), 66–82, DOI: 10.62704/10057/28466.
Search in Google Scholar Back to article
Kim, H, Lee, S, H, 2022. Prediction of burr types in drilling of al-7075 using acoustic emission and convolution neural networks. IEEE Access, 10, 67826–67838, DOI: 10.1109/ACCESS.2022.3186336.
Search in Google Scholar Back to article
Knittel, D, Makich, H, Nouari, M, 2019. Milling diagnosis using artificial intelligence approaches. Mechanics & Industry, 20(8), 809, DOI: 10.1051/meca/2020053.
Search in Google Scholar Back to article
Koch, J, Schoepflin, D, Venkatanarasimhan, A, Schüppstuhl, T, 2022. Tool wear classification in automated drilling operations of aircraft structure components using artificial intelligence methods. SAE International Journal of Advances and Current Practices in Mobility, 4(4), 1072–1081, DOI: 10.4271/2022-01-0040.
Search in Google Scholar Back to article
Kolozis, P, Galatoulas, M, Gkika, A, Koumoulos, E, 2025. Sustainability meets ai: the potential of coupling advanced materials science with life cycle assessment for industry commons. Engineering Proceedings, 90(1), DOI: 10.3390/engproc2025090092.
Search in Google Scholar Back to article
Kosova, F, Altay, Ö, Ünver, H, Ö, 2024. Structural health monitoring in aviation: a comprehensive review and future directions for machine learning. Nondestructive Testing and Evaluation, 40(1), 1–60, DOI: 10.1080/10589759.2024.2350575.
Search in Google Scholar Back to article
Kumar, A, Shrivastava, V, K, Kumar, P, Kumar, A, Gulati, V, 2024. Predictive and experimental analysis of forces in die-less forming using artificial intelligence techniques. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 09544089241235473, DOI: 10.1177/09544089241235473.
Search in Google Scholar Back to article
Kumar, K, D, Muthusamy, V, 2017. Fault tolerant control, artificial intelligence and predictive analytics for aerospace systems: an overview. Information, Communication and Computing Technology, 750, 351–362, DOI: 10.1007/978-981-10-6544-6_32.
Search in Google Scholar Back to article
Kumar, N, Shahzeb Hasan, S, Srivastava, K, Akhtar, R, Kumar Yadav, R, Choubey, V, K, 2022. Lean manufacturing techniques and its implementation: a review. Materials Today: Proceedings, 64, 1188–1192, DOI: 10.1016/j.matpr.2022.03.481.
Search in Google Scholar Back to article
Lakatos, Á, Eze, A, H, 2023. Comparison of thermal insulation performance of different materials used for aircrafts. Vehicle and Automotive Engineering 4, 346–356, DOI: 10.1007/978-3-031-15211-5_30.
Search in Google Scholar Back to article
Lawal, Z, K, Yassin, H, Zakari, R, Y, 2020. Stock market prediction using supervised machine learning techniques: an overview. 2020 IEEE Asia- Pacific Conference on Computer Science and Data Engineering (CSDE), 1–6, DOI: 10.1109/CSDE50874.2020.9411609.
Search in Google Scholar Back to article
Le Clainche, S, Ferrer, E, Gibson, S, Cross, E, Parente, A, Vinuesa, R, 2023. Improving aircraft performance using machine learning: a review. Aerospace Science and Technology, 138, 108354, DOI: 10.1016/j.ast.2023.108354.
Search in Google Scholar Back to article
Ledesma, R, Wohl, C, Grimsley, B, 2024. Crystallization kinetics analysis and modeling of aerospace paek materials. Polymer Engineering & Science, 64(8), 3802–3816, DOI: 10.1002/pen.26813.
Search in Google Scholar Back to article
Lee, S, K, H, Mongan, P, G, Farhadi, A, Hinchy, E, P, O’Dowd, N, P, McCarthy, C, T, 2025. In-situ evaluation of hole quality and cutting tool condition in robotic drilling of composite materials using machine learning. Journal of Intelligent Manufacturing, DOI: 10.1007/s10845-024-02528-7.
Search in Google Scholar Back to article
Li, C, Pyeatt, L, 2005. A short tutorial on reinforcement learning: review and applications. Intelligent Information Processing II, 163, 509–513, DOI: 10.1007/0-387-23152-8_63.
Search in Google Scholar Back to article
Li, J, King, S, Jennions, I, 2023a. Intelligent fault diagnosis of an aircraft fuel system using machine learning—a literature review. Machines, 11(4), 481, DOI: 10.3390/machines11040481.
Search in Google Scholar Back to article
Li, L, Fan, Y, Tse, M, Lin, K,-Y, 2020. A review of applications in federated learning. Computers & Industrial Engineering, 149, 106854, DOI: 10.1016/j.cie.2020.106854.
Search in Google Scholar Back to article
Li, S, Yue, X, Li, Q, Peng, H, Dong, B, Liu, T, Yang, H, Fan, J, Shu, S, Qiu, F, Jiang, Q, 2023b. Development and applications of aluminum alloys for aerospace industry. Journal of Materials Research and Technology, 27, 944–983, DOI: 10.1016/j.jmrt.2023.09.274.
Search in Google Scholar Back to article
Li, Z, Meng, Z, Liu, J, Soutis, C, Gibson, A, 2023c. Machine learningenabled thickness estimation of thin coatings on carbon fibre composites using microwaves. Measurement Science and Technology, 34(12), 124003, DOI: 10.1088/1361-6501/acf9c1.
Search in Google Scholar Back to article
Ling, S, Wu, Z, Mei, J, 2023. Comparison and review of classical and machine learning-based constitutive models for polymers used in aeronautical thermoplastic composites. REVIEWS ON ADVANCED MATERIALS SCIENCE, 62(1), 20230107, DOI: 10.1515/rams-2023-0107.
Search in Google Scholar Back to article
Liu, C, Nie, Q, 2025. A blockchain-based llm-driven energy-efficient scheduling system towards distributed multi-agent manufacturing scenario of new energy vehicles within the circular economy. Computers & Industrial Engineering, 201, 110889, DOI: 10.1016/j.cie.2025.110889.
Search in Google Scholar Back to article
Liu, W, Tian, Z, Jiang, X, Liu, S, Zhao, B, Han, Q, Li, J, Deng, J, 2022. A milling cutter state recognition method based on multi-source heterogeneous data fusion. The International Journal of Advanced Manufacturing Technology, 122(7), 3365–3378, DOI: 10.1007/s00170-022-10017-5.
Search in Google Scholar Back to article
Liu, Y, Han, T, Ma, S, Zhang, J, Yang, Y, Tian, J, He, H, Li, A, He, M, Liu, Z, Wu, Z, Zhao, L, Zhu, D, Li, X, Qiang, N, Shen, D, Liu, T, Ge, B, 2023. Summary of chatgpt-related research and perspective towards the future of large language models. Meta-Radiology, 1(2), 100017, DOI: 10.1016/j.metrad.2023.100017.
Search in Google Scholar Back to article
Llasag Rosero, R, Silva, C, Ribeiro, B, Santos, B, F, 2024. Label synchronization for hybrid federated learning in manufacturing and predictive maintenance. Journal of Intelligent Manufacturing, DOI: 10.1007/s10845-023-02298-8.
Search in Google Scholar Back to article
Lockett, H, Fletcher, S, Luquet, N, 2014. Applying design for assembly principles in computer aided design to make small changes that improve the efficiency of manual aircraft systems installations. SAE International Journal of Aerospace, 7(2), 284–291, DOI: 10.4271/2014-01-2266.
Search in Google Scholar Back to article
Loyer, J,-L, Henriques, E, Fontul, M, Wiseall, S, 2016. Comparison of machine learning methods applied to the estimation of manufacturing cost of jet engine components. International Journal of Production Economics, 178, 109–119, DOI: 10.1016/j.ijpe.2016.05.006.
Search in Google Scholar Back to article
M. A. Gorkavyy A. S. Gudim D. M. Grabar 2024. Development of a system for detecting production parts within the framework of technological processes in the aviation industry. 2024 International Russian Automation Conference (RusAutoCon), 992–997, DOI: 10.1109/RusAutoCon61949.2024.10694324.
Search in Google Scholar Back to article
M, R, Patil, R, Sarode, D, G, Sakharwade, S, A, 2025. AI-based smart control systems for induction brazing in industrial applications. 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM), 1530–1537, DOI: 10.1109/ICTMIM65579.2025.10988032.
Search in Google Scholar Back to article
Manohar, K, Hogan, T, Buttrick, J, Banerjee, A, G, Kutz, J, N, Brunton, S, L, 2018. Predicting shim gaps in aircraft assembly with machine learning and sparse sensing. Journal of Manufacturing Systems, 48, 87–95, DOI: 10.1016/j.jmsy.2018.01.011.
Search in Google Scholar Back to article
Martinez, J, MacAllister, A, Dominguez, E, Mahlmann, C, Quinlan, G, McKee, M, 2021. Applying neural networks to the f-35 seam validation process. 2021 IEEE Aerospace Conference (50100), 1–11, DOI: 10.1109/AERO50100.2021.9438381.
Search in Google Scholar Back to article
Marudhappan, R, Chandrasekhar, U, 2022. Additive manufacturing in india aerospace manufacturing and mro industry: challenges and opportunities. Journal of The Institution of Engineers (India): Series C, 103(4), 1013–1030, DOI: 10.1007/s40032-022-00812-0.
Search in Google Scholar Back to article
Massimo, D, Ganthaler, E, Buriro, A, Barile, F, Moraschini, M, Dignös, A, Villgrattner, T, Peer, A, Ricci, F, 2023. Estimation of mass and lengths of sintered workpieces using machine learning models. IEEE Transactions on Instrumentation and Measurement, 72, 1–14, DOI: 10.1109/TIM.2023.3298413.
Search in Google Scholar Back to article
Matitopanum, S, Pitakaso, R, Sethanan, K, Srichok, T, Chokanat, P, 2023a. Prediction of the ultimate tensile strength (uts) of asymmetric friction stir welding using ensemble machine learning methods. Processes, 11, 391, DOI: 10.3390/pr11020391.
Search in Google Scholar Back to article
Matitopanum, S, Luesak, P, Chiaranai, S, Pitakaso, R, Srichok, T, Sirirak, W, Jirasirilerd, G, 2023b. A predictive model for weld properties in aa- 7075-fsw: a heterogeneous amis-ensemble machine learning approach. Intelligent Systems with Applications, 19, 200259, DOI: 10.1016/j.iswa.2023.200259.
Search in Google Scholar Back to article
Mehrabi, P, Mortazavi, M, Far, H, 2025. Axisymmetric thermal postbuckling of the eccentric annular sector plate made of gorimetamaterials: introducing dnn-rf algorithm for solving the postbuckling problems. Thin-Walled Structures, 208, 112795, DOI: 10.1016/j.tws.2024.112795.
Search in Google Scholar Back to article
Meister, S, Wermes, M, Stüve, J, Groves, R, M, 2021a. Cross-evaluation of a parallel operating svm – cnn classifier for reliable internal decisionmaking processes in composite inspection. Journal of Manufacturing Systems, 60, 620–639, DOI: 10.1016/j.jmsy.2021.07.022.
Search in Google Scholar Back to article
Meister, S, Wermes, M, Stüve, J, Groves, R, M, 2021b. Investigations on explainable artificial intelligence methods for the deep learning classification of fibre layup defect in the automated composite manufacturing. Composites Part B: Engineering, 224, 109160, DOI: 10.1016/j.compositesb.2021.109160.
Search in Google Scholar Back to article
Mendes, N, Vieira, J, G, V, Mano, A, P, 2022. Risk management in aviation maintenance: a systematic literature review. Safety science, 153, 105810, DOI: 10.1016/j.ssci.2022.105810.
Search in Google Scholar Back to article
Merayo, D, Rodríguez-Prieto, A, Camacho, A, M, 2021. Topological optimization of artificial neural networks to estimate mechanical properties in metal forming using machine learning. Metals, 11(8), 1289, DOI: 10.3390/met11081289.
Search in Google Scholar Back to article
Mezher, M, T, Pereira, A, Shakir, R, A, Trzepieciński, T, 2024. Application of machine learning and neural network models based on experimental evaluation of dissimilar resistance spot-welded joints between grade 2 titanium alloy and aisi 304 stainless steel. Heliyon, 10(24), e40898, DOI: 10.1016/j.heliyon.2024.e40898.
Search in Google Scholar Back to article
Moenck, K, Koch, J, Jan-Erik Rath Büsch, L, Gierecker, J, Kähler, F, Kalscheuer, F, Masuhr, C, Kipping, J, Prünte, P, Schoepflin, D, Eschen, H, Wulff, L, A, Rodeck, R, Wende, G, Gomse, M, Schüppstuhl, T, 2025. Industry 5.0 in aircraft production and mro: technologies, challenges, and opportunities. CEAS Aeronautical Journal, DOI: 10.1007/s13272-025-00832-3.
Search in Google Scholar Back to article
Moher, D, Liberati, A, Tetzlaff, J, Altman, D, G, the PRISMA Group 2009. Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. Annals of Internal Medicine, 151(4), 264–269, DOI: 10.7326/0003-4819-151-4-200908180-00135.
Search in Google Scholar Back to article
Mohib, A, A, Azad, R, Haque, S, Hossain, F, 2017. Logistics and supply chain: an overview of business jet aircraft manufacturing. Journal of Economics and Sustainable Development, 8(10).
Search in Google Scholar Back to article
Morsi, O, E, Whealan-George, K, A, Clevenger, A, D, 2018. Assessment and comparison of aviation manufacturing industries throughout mexico and brazil. International Journal of Aviation, Aeronautics, and Aerospace, 5(1), 4, DOI: 10.15394/ijaaa.2018.1199.
Search in Google Scholar Back to article
Mosqueira-Rey, E, Hernández-Pereira, E, Alonso-Ríos, D, Bobes-Bascarán, J, Fernández-Leal, Á, 2023. Human-in-the-loop machine learning: a state of the art. Artificial Intelligence Review, 56(4), 3005–3054, DOI: 10.1007/s10462-022-10246-w.
Search in Google Scholar Back to article
Mueller, R, Vette, M, Masiak, T, Duppe, B, Schulz, A, 2019. Intelligent real time inspection of rivet quality supported by human-robot-collaboration. SAE International Journal of Advances and Current Practices in Mobility, 2(2), 811–817, DOI: 10.4271/2019-01-1886.
Search in Google Scholar Back to article
Mumali, F, 2022. Artificial neural network-based decision support systems in manufacturing processes: a systematic literature review. Computers & Industrial Engineering, 165, 107964, DOI: 10.1016/j.cie.2022.107964.
Search in Google Scholar Back to article
Mustapha, K, B, 2025. A survey of emerging applications of large language models for problems in mechanics, product design, and manufacturing. Advanced Engineering Informatics, 64, 103066, DOI: 10.1016/j.aei.2024.103066.
Search in Google Scholar Back to article
Naeem, S, Ali, A, Anam, S, Ahmed, M, M, 2023. An unsupervised machine learning algorithms: comprehensive review. International Journal of Computing and Digital Systems, 13(1), 911–921, DOI: 10.12785/ijcds/130172.
Search in Google Scholar Back to article
Nahavandi, S, 2019. Industry 5.0—a human-centric solution. Sustainability, 11(16), 4371, DOI: 10.3390/su11164371.
Search in Google Scholar Back to article
Nalajam, P, K, V, R, 2021. Microstructural porosity segmentation using machine learning techniques in wire-based direct energy deposition of aa6061. Micron, 151, 103161, DOI: 10.1016/j.micron.2021.103161.
Search in Google Scholar Back to article
Nargundkar, A, Kumar, S, Bongale, A, 2024. Multi-objective optimization of friction stir processing tool with composite material parameters. Lubricants, 12(12), DOI: 10.3390/lubricants12120428.
Search in Google Scholar Back to article
Narkhede, G, B, Pasi, B, N, Rajhans, N, Kulkarni, A, 2024. Industry 5.0 and sustainable manufacturing: a systematic literature review. Benchmarking: An International Journal, 32(2), 608–635, DOI: 10.1108/BIJ-03-2023-0196.
Search in Google Scholar Back to article
Natarajan, M, Pasupuleti, T, Giri, J, Sunheriya, N, Katta, L, N, Chadge, R, Mahatme, C, Giri, P, Mallik, S, Ray, K, 2023. Machinability of titanium grade 5 alloy for wire electrical discharge machining using a hybrid learning algorithm. Information, 14(8), 439, DOI: 10.3390/info14080439.
Search in Google Scholar Back to article
Niccolai, A, Caputo, D, Chieco, L, Grimaccia, F, Mussetta, M, 2021. Machine learning-based detection technique for ndt in industrial manufacturing. Mathematics, 9(11), 1251, DOI: 10.3390/math9111251.
Search in Google Scholar Back to article
Nkemdilim, M, N, Uzoamaka, P, R, Daniel, U, Chidi, M, K, 2024. An overview of supervised machine learning paradigms and their classifiers. International Journal of Advanced Engineering, Management and Science, 10(3), 24–32, DOI: 10.22161/ijaems.103.4.
Search in Google Scholar Back to article
Nti, I, K, Adekoya, A, F, Weyori, B, A, Nyarko-Boateng, O, 2022. Applications of artificial intelligence in engineering and manufacturing: a systematic review. Journal of Intelligent Manufacturing, 33(6), 1581–1601, DOI: 10.1007/s10845-021-01771-6.
Search in Google Scholar Back to article
Oliveira, R, M, A, Sant’Anna, Â, M, O, Silva, P, H, F, da 2024. Explainable machine learning models for defects detection in industrial processes. Computers & Industrial Engineering, 192, 110214, DOI: 10.1016/j.cie.2024.110214.
Search in Google Scholar Back to article
Olowe, M, Ogunsanya, M, Best, B, Hanif, Y, Bajaj, S, Vakkalagadda, V, Fatoki, O, Desai, S, 2024. Spectral features analysis for print quality prediction in additive manufacturing: an acoustics-based approach. Sensors, 24(15), 4864, DOI: 10.3390/s24154864.
Search in Google Scholar Back to article
P, R, Shruti, J, R, Thippeswamy, I, B L, N, V, D, 2024. A surrogate approach to explainable ai for predictive maintenance: techniques and applications. 2024 5th International Conference on Circuits, Control, Communication and Computing (I4C), 160–165, DOI: 10.1109/I4C62240.2024.10748524.
Search in Google Scholar Back to article
Page, M, J, McKenzie, J, E, Bossuyt, P, M, Boutron, I, Hoffmann, T, C, Mulrow, C, D, Shamseer, L, Tetzlaff, J, M, Akl, E, A, Brennan, S, E, Chou, R, Glanville, J, Grimshaw, J, M, Hróbjartsson, A, Lalu, M, M, Li, T, Loder, E, W, Mayo-Wilson, E, McDonald, S, McGuinness, L, A, Stewart, L, A, Thomas, J, Tricco, A, C, Welch, V, A, Whiting, P, Moher, D, 2021. The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, DOI: 10.1136/bmj.n71.
Search in Google Scholar Back to article
Paleyes, A, Urma, R,-G, Lawrence, N, D, 2023. Challenges in deploying machine learning: a survey of case studies. ACM Computing Surveys, 55(6), 1–29, DOI: 10.1145/3533378.
Search in Google Scholar Back to article
Pattanam Ramamoorthy, K, Balasubramani, S, Dinesh, S, Arul, M, 2025. Intelligent hybrid optimization of wedm for ti-6al-4v using taguchi deep learning and anova for enhanced surface integrity and prediction accuracy. Materials and Technology, 59(3), 443–450, DOI: 10.17222/mit.2025.1393.
Search in Google Scholar Back to article
Perani, M, Baraldo, S, Decker, M, Vandone, A, Valente, A, Paoli, B, 2023. Track geometry prediction for laser metal deposition based on on-line artificial vision and deep neural networks. Robotics and Computer- Integrated Manufacturing, 79, 102445, DOI: 10.1016/j.rcim.2022.102445.
Search in Google Scholar Back to article
Pereira, S, Baptista, M, Henriques, E, M, P, 2018. Data-driven quality prognostics for automated riveting processes. 2018 IEEE Aerospace Conference, 1–15, DOI: 10.1109/AERO.2018.8396547.
Search in Google Scholar Back to article
Plathottam, S, J, Rzonca, A, Lakhnori, R, Iloeje, C, O, 2023. A review of artificial intelligence applications in manufacturing operations. Journal of Advanced Manufacturing and Processing, 5(3), e10159, DOI: 10.1002/amp2.10159.
Search in Google Scholar Back to article
Prakash, K, S, Nancharaih, T, Rao, V, V, S, 2018. Additive manufacturing techniques in manufacturing -an overview. Materials Today: Proceedings, 5(2), 3873–3882, DOI: 10.1016/j.matpr.2017.11.642.
Search in Google Scholar Back to article
Prasomthong, S, Phatungthane, T, Chomchalao, C, 2025. Optimization of aluminum matrix composite production in a friction stir process with mgo nanoparticle reinforcement using ann-gra modeling. Engineering and Applied Science Research, 52(3), 282–295.
Search in Google Scholar Back to article
Prudviraj, K, Deshmukh, S, Tripathy, R, K, Supradeepan, K, Tandon, P, Jha, P, K, 2021. Machine learning-based approach for the prediction of an orifice size of aerospace vehicle rcs thrusters during cold flow calibration. 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA), 455–459, DOI: 10.1109/ICCCA52192.2021.9666377.
Search in Google Scholar Back to article
Pussegoda, K, Turner, L, Garritty, C, Mayhew, A, Skidmore, B, Stevens, A, Boutron, I, Sarkis-Onofre, R, Bjerre, L, M, Hróbjartsson, A, Altman, D, G, Moher, D, 2017. Systematic review adherence to methodological or reporting quality. Systematic Reviews, 6(1), 131, DOI: 10.1186/s13643-017-0527-2.
Search in Google Scholar Back to article
Rabby, M, M, Das, P, P, Rahman, M, Vadlamudi, V, Raihan, R, 2024. Fast and accurate prediction of cure quality and mechanical performance in fiber‐reinforced polymer composite using dielectric variables and machine learning. Polymer Composites, 45(2), 1810–1825, DOI: 10.1002/pc.27891.
Search in Google Scholar Back to article
Rahaman, Md, J, 2024. A comprehensive review to understand the definitions, advantages, disadvantages and applications of machine learning algorithms. International Journal of Computer Applications, 186(31), 43–47, DOI: 10.5120/ijca2024923868.
Search in Google Scholar Back to article
Rahm, E, Do, H, H, 2000. Data cleaning: problems and current approaches. IEEE Data Eng. Bull., 23(4), 3–13.
Search in Google Scholar Back to article
Rahman, M, Z, Rahman, M, Mahbub, T, Ashiquzzaman, M, Sagadevan, S, Hoque, M, E, 2023. Advanced biopolymers for automobile and aviation engineering applications. Journal of Polymer Research, 30(3), 106, DOI: 10.1007/s10965-023-03440-z.
Search in Google Scholar Back to article
Raj, R, J, Selvam, P, P, Pughalendi, M, 2021. A review of aluminum alloys in aircraft and aerospace industry. Journal of Huazhong University of Science and Technology, 50(4).
Search in Google Scholar Back to article
Raju Chekuri, R, B, Tak, T, K, Kshirsagar, P, R, Sharma, A, K, Sivaneasan, B, 2025. MVHFC-net: ai-driven multi variate hydraulic fault detection and classification network in hydraulic systems. International Journal of System Assurance Engineering and Management, 16(5), 1770–1796, DOI: 10.1007/s13198-025-02769-6.
Search in Google Scholar Back to article
Ramezankhani, M, Crawford, B, Narayan, A, Voggenreiter, H, Seethaler, R, Milani, A, S, 2021a. Making costly manufacturing smart with transfer learning under limited data: a case study on composites autoclave processing. Journal of Manufacturing Systems, 59, 345–354, DOI: 10.1016/j.jmsy.2021.02.015.
Search in Google Scholar Back to article
Ramezankhani, M, Narayan, A, Seethaler, R, Milani, A, S, 2021b. An active transfer learning (atl) framework for smart manufacturing with limited data: case study on material transfer in composites processing. 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), 277–282, DOI: 10.1109/ICPS49255.2021.9468145.
Search in Google Scholar Back to article
Ramírez-Sanz, J, M, Maestro-Prieto, J,-A, Arnaiz-González, Á, Bustillo, A, 2023. Semi-supervised learning for industrial fault detection and diagnosis: a systemic review. ISA Transactions, 143, 255–270, DOI: 10.1016/j.isatra.2023.09.027.
Search in Google Scholar Back to article
Raoofi, T, Yildiz, M, 2023. Comprehensive review of battery state estimation strategies using machine learning for battery management systems of aircraft propulsion batteries. Journal of Energy Storage, 59, 106486, DOI: 10.1016/j.est.2022.106486.
Search in Google Scholar Back to article
Raouf, I, Kumar, P, Cheon, Y, Tanveer, M, Jo, S,-H, Kim, H, S, 2025. Advances in prognostics and health management for aircraft landing gear—progress, challenges, and future possibilities. International Journal of Precision Engineering and Manufacturing-Green Technology, 12, 301–320, DOI: 10.1007/s40684-024-00646-4.
Search in Google Scholar Back to article
Rasamoelina, A, D, Adjailia, F, Sincak, P, 2020. A review of activation function for artificial neural network. 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI), 281–286, DOI: 10.1109/SAMI48414.2020.9108717.
Search in Google Scholar Back to article
Rasiya, G, Shukla, A, Saran, K, 2021. Additive manufacturing-a review. Materials Today: Proceedings, 47, 6896–6901, DOI: 10.1016/j.matpr.2021.05.181.
Search in Google Scholar Back to article
Raz, A, K, Blasch, E, P, Guariniello, C, Mian, Z, T, 2021. An overview of systems engineering challenges for designing ai-enabled aerospace systems. AIAA Scitech 2021 Forum, DOI: 10.2514/6.2021-0564.
Search in Google Scholar Back to article
Rocha, F, M, Costa, V, S, Reis, L, P, 2020. From reinforcement learning towards artificial general intelligence. Trends and Innovations in Information Systems and Technologies, 1160, 401–413, DOI: 10.1007/978-3-030-45691-7_37.
Search in Google Scholar Back to article
Rolf, B, Beier, A, Jackson, I, Müller, M, Reggelin, T, Stuckenschmidt, H, Lang, S, 2024. A review on unsupervised learning algorithms and applications in supply chain management. International Journal of Production Research, 1–51, DOI: 10.1080/00207543.2024.2390968.
Search in Google Scholar Back to article
Román, A, J, Qin, S, Rodríguez, J, C, Zavala, V, Osswald, T, A, 2023. Viscoelastic behavior prediction of natural rubber blends via machine learning and its application for reverse engineering materials. , 2884, 150001, DOI: 10.1063/5.0168411.
Search in Google Scholar Back to article
Rosa, R, C, Gonçalves, M, C, Barbalho, S, C, M, 2025. Machine learning applied to forecasting the manufacturing time of new products prototypes and eto products: an exploratory study. International Journal of Production Economics, 287, 109688, DOI: 10.1016/j.ijpe.2025.109688.
Search in Google Scholar Back to article
Ruiz, L, Gómez, A, Díaz, S, González, J, M, Cavas, F, 2022. Fixing elements localization in aircraft large structures using machine learning techniques. Advances in Design Engineering II, 139–146, DOI: 10.1007/978-3-030-92426-3_17.
Search in Google Scholar Back to article
Ruiz, L, Torres, M, Gómez, A, Díaz, S, González, J, M, Cavas, F, 2020. Detection and classification of aircraft fixation elements during manufacturing processes using a convolutional neural network. Applied Sciences, 10(19), 6856, DOI: 10.3390/app10196856.
Search in Google Scholar Back to article
Saadi, M, Bernier, V, Zacharewicz, G, Daclin, N, 2024. Surrogate modelling with deep learning for optimizing manufacturing assembly lines. 2024 Annual Modeling and Simulation Conference (ANNSIM), 1–12, DOI: 10.23919/ANNSIM61499.2024.10732237.
Search in Google Scholar Back to article
Saghiri, A, M, Vahidipour, S, M, Jabbarpour, M, R, Sookhak, M, Forestiero, A, 2022. A survey of artificial intelligence challenges: analyzing the definitions, relationships, and evolutions. Applied Sciences, 12(8), 4054, DOI: 10.3390/app12084054.
Search in Google Scholar Back to article
Salary, R, (Ross) Lombardi, J, P, Weerawarne, D, L, Tootooni, M, S, Rao, P, K, Poliks, M, D, 2020. A sparse representation classification approach for near real-time, physics-based functional monitoring of aerosol jet-fabricated electronics. Journal of Manufacturing Science and Engineering, 142(8), 081007, DOI: 10.1115/1.4047045.
Search in Google Scholar Back to article
Sanchez, J, A, Conde, A, Arriandiaga, A, Wang, J, Plaza, S, 2018. Unexpected event prediction in wire electrical discharge machining using deep learning techniques. Materials, 11(7), 1100, DOI: 10.3390/ma11071100.
Search in Google Scholar Back to article
Şap, S, Acar, E, Değirmenci, Ü, Usca, Ü, A, Memiş, S, Şener, R, 2025. Machinability of different cu-gr composites in milling: performance parameters prediction via machine learning models. Expert Systems with Applications, 272, 126770, DOI: 10.1016/j.eswa.2025.126770.
Search in Google Scholar Back to article
Sarh, B, Buttrick, J, Munk, C, Bossi, R, 2009. Aircraft manufacturing and assembly. Springer handbook of automation, 893–910, DOI: 10.1007/978-3-540-78831-7_51.
Search in Google Scholar Back to article
Schmitt, D, Gollnick, V, 2016. Aircraft manufacturer. Air Transport System, 157–186, DOI: 10.1007/978-3-7091-1880-1_6.
Search in Google Scholar Back to article
Sen, M, C, Alkan, M, 2025. Time-series prediction of failures in an industrial assembly line using artificial learning. Applied Sciences, 15(11), DOI: 10.3390/app15115984.
Search in Google Scholar Back to article
Shaban, Y, Meshreki, M, Yacout, S, Balazinski, M, Attia, H, 2017. Process control based on pattern recognition for routing carbon fiber reinforced polymer. Journal of Intelligent Manufacturing, 28(1), 165–179, DOI: 10.1007/s10845-014-0968-6.
Search in Google Scholar Back to article
Shafi, I, Mazhar, M, F, Fatima, A, Alvarez, R, M, Miró, Y, Espinosa, J, C, M, Ashraf, I, 2023. Deep learning-based real time defect detection for optimization of aircraft manufacturing and control performance. Drones, 7(1), 31, DOI: 10.3390/drones7010031.
Search in Google Scholar Back to article
She, B, Li, F, Cui, H, Zhang, J, Bo, R, 2023. Fusion of microgrid control with model-free reinforcement learning: review and vision. IEEE Transactions on Smart Grid, 14(4), 3232–3245, DOI: 10.1109/TSG.2022.3222323.
Search in Google Scholar Back to article
Shivi Kesarwani 2017. Polymer composites in aviation sector. International Journal of Engineering Research and, V6(06), IJERTV6IS060291, DOI: 10.17577/IJERTV6IS060291.
Search in Google Scholar Back to article
Shubham Verma Meenu Gupta Joy Prakash Misra 2018. Performance evaluation of friction stir welding using machine learning approaches. MethodsX, 5, 1048–1058, DOI: 10.1016/j.mex.2018.09.002.
Search in Google Scholar Back to article
Simon, D, L, 2010. Propulsion diagnostic method evaluation strategy (prodimes) user’s guide. .
Search in Google Scholar Back to article
Singamneni, S, Lv, Y, Hewitt, A, Chalk, R, Thomas, W, Jordison, D, 2019. Additive manufacturing for the aircraft industry: a review. Journal of Aeronautics & Aerospace Engineering, 08(1), DOI: 10.35248/2168-9792.19.8.215.
Search in Google Scholar Back to article
Siyaev, A, Valiev, D, Jo, G,-S, 2023. Interaction with industrial digital twin using neuro-symbolic reasoning. Sensors, 23(3), 1729, DOI: 10.3390/s23031729.
Search in Google Scholar Back to article
Song, Z, Yang, X, Xu, Z, King, I, 2023. Graph-based semi-supervised learning: a comprehensive review. IEEE Transactions on Neural Networks and Learning Systems, 34(11), 8174–8194, DOI: 10.1109/TNNLS.2022.3155478.
Search in Google Scholar Back to article
Soutis, C, 2005. Fibre reinforced composites in aircraft construction. Progress in Aerospace Sciences, 41(2), 143–151, DOI: 10.1016/j.paerosci.2005.02.004.
Search in Google Scholar Back to article
Strube, G, Eloot, K, Griessmann, N, Dhawan, R, Ramaswamy, S, 2017. Trends in the commercial aerospace industry. Supply Chain Integration Challenges in Commercial Aerospace: A Comprehensive Perspective on the Aviation Value Chain, 141–159, DOI: 10.1007/978-3-319-46155-7_10.
Search in Google Scholar Back to article
Sudhaman, K, Akuthota, M, Chaurasiya, S, K, 2022. A review on the different regression analysis in supervised learning. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications, 15–32, DOI: 10.1201/9781003164265-2.
Search in Google Scholar Back to article
Talib, S, 2023. Computational engineering advancements: general review of mathematical modeling in computer engineering applications. Al Rafidain Journal of Engineering Sciences, 2(1), 51–71, DOI: 10.61268/h1dg2e95.
Search in Google Scholar Back to article
Thanusha, D, Balguri, P, K, 2024. Optimizing wear and mechanical characteristics of jute fiber composites reinforced with silicon carbide and prediction using neural network technique. AIP Conference Proceedings, 3007, 100032, DOI: 10.1063/5.0196585.
Search in Google Scholar Back to article
Tong, M, T, 2023. Aero-engines ai - a machine-learning app for aircraft engine concepts assessment. ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition, Volume 1: Aircraft Engine, V001T01A017, DOI: 10.1115/GT2023-102024.
Search in Google Scholar Back to article
Tsuzuki, R, 2022. Development of automation and artificial intelligence technology for welding and inspection process in aircraft industry. Welding in the World, 66(1), 105–116, DOI: 10.1007/s40194-021-01210-3.
Search in Google Scholar Back to article
Tyystjärvi, T, Fridolf, P, Rosell, A, Virkkunen, I, 2024. Deploying machine learning for radiography of aerospace welds. Journal of Nondestructive Evaluation, 43(1), 24, DOI: 10.1007/s10921-023-01041-w.
Search in Google Scholar Back to article
Van Nguyen, T, H, Huang, P,-M, Chien, C,-F, Chang, C,-K, 2023. Digital transformation for cost estimation system via meta-learning and an empirical study in aerospace industry. Computers & Industrial Engineering, 184, 109558, DOI: 10.1016/j.cie.2023.109558.
Search in Google Scholar Back to article
Van Otterlo, M, 2002. Relational representations in reinforcement learning: review and open problems. Proceedings of the ICML, 2.
Search in Google Scholar Back to article
Varol Özkavak, H, İnce, M, Bıçaklı, E, E, 2023. Prediction of mechanical properties of the 2024 aluminum alloy by using machine learning methods. Arabian Journal for Science and Engineering, 48(3), 2841–2850, DOI: 10.1007/s13369-022-07009-8.
Search in Google Scholar Back to article
Veeramani, S, Muthuswamy, S, Sagar, K, Zoppi, M, 2020. Artificial intelligence planners for multi-head path planning of swarmitfix agents. Journal of Intelligent Manufacturing, 31(4), 815–832, DOI: 10.1007/s10845-019-01479-8.
Search in Google Scholar Back to article
Verma, S, Misra, J, P, Popli, D, 2022. Modeling of friction stir welding of aviation grade aluminium alloy using machine learning approaches. International Journal of Modelling and Simulation, 42(1), 1–8, DOI: 10.1080/02286203.2020.1803605.
Search in Google Scholar Back to article
Vieira, D, R, Vieira, R, K, Chang Chain, M, 2017. Strategy and management for the recycling of carbon fiber-reinforced polymers (cfrps) in the aircraft industry: a critical review. International Journal of Sustainable Development & World Ecology, 24(3), 214–223, DOI: 10.1080/13504509.2016.1204371.
Search in Google Scholar Back to article
Wang, J, Sánchez, J, A, Iturrioz, J, A, Ayesta, I, 2019. Artificial intelligence for advanced non-conventional machining processes. Procedia Manufacturing, 41, 453–459, DOI: 10.1016/j.promfg.2019.09.032.
Search in Google Scholar Back to article
Wang, J, Sanchez, J, A, Ayesta, I, Iturrioz, J, A, 2018a. Unsupervised machine learning for advanced tolerance monitoring of wire electrical discharge machining of disc turbine fir-tree slots. Sensors, 18(10), 3359, DOI: 10.3390/s18103359.
Search in Google Scholar Back to article
Wang, J, Sanchez, J, Iturrioz, J, Ayesta, I, 2018b. Geometrical defect detection in the wire electrical discharge machining of fir-tree slots using deep learning techniques. Applied Sciences, 9(1), 90, DOI: 10.3390/app9010090.
Search in Google Scholar Back to article
Wang, P, Karigiannis, J, Gao, R, X, 2024. Ontology-integrated tuning of large language model for intelligent maintenance. CIRP Annals, 73(1), 361–364, DOI: 10.1016/j.cirp.2024.04.012.
Search in Google Scholar Back to article
Wang, Y, Wang, F, Wang, Z, 2020. An intelligent diagnosis algorithm for oxygen decompressor manufacturing process. Innovative Computing, 675, 457–469, DOI: 10.1007/978-981-15-5959-4_56.
Search in Google Scholar Back to article
Weiss, K, Khoshgoftaar, T, M, Wang, D, 2016. A survey of transfer learning. Journal of Big Data, 3(1), 9, DOI: 10.1186/s40537-016-0043-6.
Search in Google Scholar Back to article
Welch, J, 2021. Will boeing soar again? navigating a corporate recovery process. Journal of Business Strategy, 42(5), 323–331, DOI: 10.1108/JBS-04-2020-0087.
Search in Google Scholar Back to article
Wen, J, Zhang, Z, Lan, Y, Cui, Z, Cai, J, Zhang, W, 2023. A survey on federated learning: challenges and applications. International Journal of Machine Learning and Cybernetics, 14(2), 513–535, DOI: 10.1007/s13042-022-01647-y.
Search in Google Scholar Back to article
Wijaya, C, Liu, J,-Z, Wang, I,-J, King, J,-K, Yang, C,-T, 2025. Toward predictive maintenance for aerospace hot press furnace and the current monitoring with machine learning. IEEE Access, 13, 110689–110708, DOI: 10.1109/ACCESS.2025.3580729.
Search in Google Scholar Back to article
Wu, C, Zhang, R, Zhao, P, Li, L, Zhang, D, 2024a. Curing simulation and data-driven curing curve prediction of thermoset composites. Scientific Reports, 14(1), 31860, DOI: 10.1038/s41598-024-83379-3.
Search in Google Scholar Back to article
Wu, Q, Wang, H, Li, B, 2024b. Predicting the fatigue life of additively manufactured alsi10mg alloy using a physics-informed neural network incorporating continuous damage mechanics. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 0(0), DOI: 10.1177/09544054241290283.
Search in Google Scholar Back to article
Wu, Y, Feng, J, 2018. Development and application of artificial neural network. Wireless Personal Communications, 102(2), 1645–1656, DOI: 10.1007/s11277-017-5224-x.
Search in Google Scholar Back to article
Xie, S, 2024. Advancing predictive maintenance research trends: using artificial intelligence for enhanced industrial reliability. 2024 IEEE International Conference on Future Machine Learning and Data Science (FMLDS), 283–288, DOI: 10.1109/FMLDS63805.2024.00059.
Search in Google Scholar Back to article
Yadam, G, Moharir, A, K, Srivastava, I, 2020. Explainable and visually interpretable machine learning for flight sciences. 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 1–6, DOI: 10.1109/CONECCT50063.2020.9198505.
Search in Google Scholar Back to article
Yan, J, Wang, X, 2022. Unsupervised and semi-supervised learning: the next frontier in machine learning for plant systems biology. The Plant Journal, 111(6), 1527–1538, DOI: 10.1111/tpj.15905.
Search in Google Scholar Back to article
Yang, Q, Cao, X, Ye, Q, 2020. Rotating machinery vibration fault diagnosis model based on autoencoder. Proceedings of the 2020 International Conference on Aviation Safety and Information Technology, 393–398, DOI: 10.1145/3434581.3434732.
Search in Google Scholar Back to article
Yasuda, Y, D, Cappabianco, F, A, Martins, L, E, G, Gripp, J, A, 2022. Aircraft visual inspection: a systematic literature review. Computers in Industry, 141, 103695, DOI: 10.1016/j.compind.2022.103695.
Search in Google Scholar Back to article
Yau, K,-L, A, Saleem, Y, Chong, Y,-W, Fan, X, Eyu, J, M, Chieng, D, 2024. The augmented intelligence perspective on human-in-the-loop reinforcement learning: review, concept designs, and future directions. IEEE Transactions on Human-Machine Systems, 54(6), 762–777, DOI: 10.1109/THMS.2024.3467370.
Search in Google Scholar Back to article
Yin, X, Zhu, Y, Hu, J, 2022. A comprehensive survey of privacy-preserving federated learning: a taxonomy, review, and future directions. ACM Computing Surveys, 54(6), 1–36, DOI: 10.1145/3460427.
Search in Google Scholar Back to article
Yüce, Z, Yayla, P, Taşkin, A, 2023. A comparative investigation of random forest regression and artificial neural networks for predicting crack growth life of a fighter aircraft wing joint under spectrum loading. Defence Science Journal, 74(01), 119–126, DOI: 10.14429/dsj.74.18954.
Search in Google Scholar Back to article
Yunker, A, Kettimuthu, R, Kral, Z, 2024. OC-dicam: one class defect identification in composite aerostructure material. ASME 2024 Aerospace Structures, Structural Dynamics, and Materials Conference, V001T01A013, DOI: 10.1115/SSDM2024-121466.
Search in Google Scholar Back to article
Yutong, L, Junqi, B, Yuxuan, Y, Dejun, N, 2024. Insights into the development trends of industrial large language models. Trends in Computer Science and Information Technology, 9(3), 76–80, DOI: 10.17352/tcsit.000084.
Search in Google Scholar Back to article
Zha, D, Bhat, Z, P, Lai, K,-H, Yang, F, Jiang, Z, Zhong, S, Hu, X, 2025. Data-centric artificial intelligence: a survey. ACM Computing Surveys, 57(5), 1–42, DOI: 10.1145/3711118.
Search in Google Scholar Back to article
Zhan, Z, Li, H, 2021a. Machine learning based fatigue life prediction with effects of additive manufacturing process parameters for printed ss 316l. International Journal of Fatigue, 142, 105941, DOI: 10.1016/j.ijfatigue.2020.105941.
Search in Google Scholar Back to article
Zhan, Z, Li, H, 2021b. A novel approach based on the elastoplastic fatigue damage and machine learning models for life prediction of aerospace alloy parts fabricated by additive manufacturing. International Journal of Fatigue, 145, 106089, DOI: 10.1016/j.ijfatigue.2020.106089.
Search in Google Scholar Back to article
Zhang, K, Ma, L, Song, Z, Gao, H, Zhou, W, Liu, J, Tao, R, 2022a. Strength prediction and progressive damage analysis of carbon fiber reinforced polymer-laminate with circular holes by an efficient artificial neural network. Composite Structures, 296, 115835, DOI: 10.1016/j.compstruct.2022.115835.
Search in Google Scholar Back to article
Zhang, X, Liang, E, 2019. Metal additive manufacturing in aircraft: current application, opportunities and challenges. IOP Conference Series: Materials Science and Engineering, 493, 012032, DOI: 10.1088/1757-899X/493/1/012032.
Search in Google Scholar Back to article
Zhang, Z, Yang, Z, Sisson, R, D, Liang, J, 2022b. Improving ceramic additive manufacturing via machine learning-enabled closed-loop control. International Journal of Applied Ceramic Technology, 19(2), 957–967, DOI: 10.1111/ijac.13976.
Search in Google Scholar Back to article
Zhang, Z, Xu, Z, 2023. Fatigue database of additively manufactured alloys. Scientific Data, 10(1), 249, DOI: 10.1038/s41597-023-02150-x.
Search in Google Scholar Back to article
Zhao, Y, Liu, Y, Lin, Q, Pan, W, Yu, W, Ren, Y, Liu, S, 2024. A conditional generative model for end-to-end stress field prediction of composite bolted joints. Engineering Applications of Artificial Intelligence, 134, 108692, DOI: 10.1016/j.engappai.2024.108692.
Search in Google Scholar Back to article
Zhu, Y, Wu, X, Gotawala, N, Higdon, D, M, Yu, H, Z, 2024. Thermal prediction of additive friction stir deposition through bayesian learningenabled explainable artificial intelligence. Journal of Manufacturing Systems, 72, 1–15, DOI: 10.1016/j.jmsy.2023.10.015.
Search in Google Scholar Back to article
Zhuang, F, Qi, Z, Duan, K, Xi, D, Zhu, Y, Zhu, H, Xiong, H, He, Q, 2021. A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 43–76, DOI: 10.1109/JPROC.2020.3004555.
Search in Google Scholar Back to article
Zolkin, A, L, Galanskiy, S, A, Kuzmin, A, M, 2021. Perspectives for use of composite and polymer materials in aircraft construction. IOP Conference Series: Materials Science and Engineering, 1047(1), 012023, DOI: 10.1088/1757-899X/1047/1/012023.
Search in Google Scholar Back to article
Zonta, T, Costa, C, A, da Righi, R, da R, Lima, M, J, de Trindade, E, S, da Li, G, P, 2020. Predictive maintenance in the industry 4.0: a systematic literature review. Computers & Industrial Engineering, 150, 106889, DOI: 10.1016/j.cie.2020.106889.
Search in Google Scholar Back to article
Zubair, S, W, H, Arafat, S, M, Khan, S, A, Niazi, S, G, Rehan, M, Arshad, M, U, Hayat, N, Aized, T, Uddin, G, M, Riaz, F, 2024. Coupling taguchi experimental designs with deep adaptive learning enhanced ai process models for experimental cost savings in manufacturing process development. Scientific Reports, 14(1), 23428, DOI: 10.1038/s41598-024-73669-1.
Search in Google Scholar Back to article
Zubair, S, W, H, Arafat, S, M, Khan, S, A, Uddin, G, M, Hayat, N, 2023. Dry finishing turning of aa7075 with binary and ternary nitrides and carbides ceramic-coated tools. The International Journal of Advanced Manufacturing Technology, 129(1), 65–87, DOI: 10.1007/s00170-023-12105-6.
Search in Google Scholar Back to article
Abaqus finite element analysis simulia - dassault systèmes. Available at: https://www.3ds.com/products/simulia/abaqus, Accessed Apr. 10, 2025.
Search in Google Scholar Back to article
Ansys mechanical structural fea analysis software. Available at: https://www.ansys.com/products/structures/ansys-mechanical, Accessed Apr. 10, 2025.
Search in Google Scholar Back to article
AnyLogic: simulation modeling software tools & solutions. Available at: https://www.anylogic.com/, Accessed Aug. 7, 2025.
Search in Google Scholar Back to article
Beijing orient institute of measurement and test (boimt). Available at: https://www.cast.cn/english/channel/1811, Accessed Apr. 14, 2025.
Search in Google Scholar Back to article
China academy of space technology. Available at: https://www.cast.cn/english, Accessed Apr. 14, 2025.
Search in Google Scholar Back to article
CMAPSS jet engine simulated data - nasa open data portal. Available at: https://data.nasa.gov/dataset/cmapss-jet-engine-simulated-data, Accessed Aug. 6, 2025.
Search in Google Scholar Back to article
Desai, A, 2025. Archd3sai/predictive-maintenance-of-aircraft-engine. Available at: https://github.com/archd3sai/Predictive-Maintenance-of-Aircraft-Engine, Accessed Apr. 14, 2025.
Search in Google Scholar Back to article
Fashion-pattern-images. Available at: https://www.kaggle.com/datasets/kimduhan/fashion-pattern-images, Accessed Aug. 6, 2025.
Search in Google Scholar Back to article
GC10-det. Available at: https://www.kaggle.com/datasets/alex000kim/gc10det, Accessed Aug. 6, 2025.
Search in Google Scholar Back to article
JavaFoil. Available at: https://www.mh-aerotools.de/airfoils/javafoil.htm, Accessed Aug. 6, 2025.
Search in Google Scholar Back to article
Kylberg texture dataset. Available at: https://www.kaggle.com/datasets/phattanunthabarsa/kylberg-texturedataset, Accessed Aug. 6, 2025.
Search in Google Scholar Back to article
Machine predictive maintenance classification. Available at: https://www.kaggle.com/datasets/shivamb/machine-predictivemaintenance-classification, Accessed Aug. 6, 2025.
Search in Google Scholar Back to article
Mechanical tools classification dataset. Available at: https://www.kaggle.com/datasets/salmaneunus/mechanical-toolsdataset, Accessed Apr. 10, 2025.
Search in Google Scholar Back to article
Microsoft Corporation Microsoft excel. Available at: https://office.microsoft.com/excel.
Search in Google Scholar Back to article
Moldex3D plastic injection molding simulation software. Available at: https://www.moldex3d.com/, Accessed Apr. 9, 2025.
Search in Google Scholar Back to article
Mueller, A, C, 2024. Wordcloud. , DOI: 10.5281/zenodo.14062883.
Search in Google Scholar Back to article
Nations, U, 2024. Member states. In: United Nations. Available at: https://www.un.org/en/about-us/member-states, Accessed Sept. 16, 2024.
Search in Google Scholar Back to article
Namilae, S, Deepak, K, Pragathi, A, Medora, E, 2024. In-situ characterization data for carbon fiber composite manufacturing. Available at: https://zenodo.org/records/10819180, Accessed Aug. 6, 2025.
Search in Google Scholar Back to article
Nektar++. Available at: https://www.nektar.info/, Accessed Apr. 14, 2025.
Search in Google Scholar Back to article
NTop computational design software formerly ntopology. Available at: https://www.ntop.com/, Accessed Aug. 6, 2025.
Search in Google Scholar Back to article
Official adobe photoshop. Available at: https://www.adobe.com/products/photoshop.html, Accessed Aug. 14, 2025.
Search in Google Scholar Back to article
Plateforme de fabrication kamp ai. Available at: https://www.kamp-ai.kr/, Accessed Aug. 6, 2025.
Search in Google Scholar Back to article
Prognostics center of excellence data set repository - nasa. Available at: https://www.nasa.gov/intelligent-systems-division/discovery-andsystems-health/pcoe/pcoe-data-set-repository/, Accessed Apr. 14, 2025.
Search in Google Scholar Back to article
Purohit, H, Tanabe, R, Ichige, K, Endo, T, Nikaido, Y, Suefusa, K, Kawaguchi, Y, 2019. MIMII dataset: sound dataset for malfunctioning industrial machine investigation and inspection. Available at: https://arxiv.org/abs/1909.09347.
Search in Google Scholar Back to article
RAVEN simulation software convergent. Available at: https://www.convergent.ca/products/raven-simulation-software, Accessed Apr. 9, 2025.
Search in Google Scholar Back to article
Sentaurus device: multidimensional (1d/2d/3d) device simulator synopsys. Available at: https://www.synopsys.com/manufacturing/tcad/devicesimulation/sentaurus-device.html, Accessed Aug. 6, 2025.
Search in Google Scholar Back to article
SOLIDWORKS. Available at: https://www.solidworks.com/, Accessed Apr. 10, 2025.
Search in Google Scholar Back to article
UIUC airfoil data site. Available at: https://mselig.ae.illinois.edu/ads/coord_database.html, Accessed Aug. 6, 2025.
Search in Google Scholar Back to article
Wu, Q, NEU-det. Available at: https://ieee-dataport.org/documents/neu-det, Accessed Aug. 6, 2025.
Search in Google Scholar Back to article

Industrial Applications of AI in Aircraft Manufacturing: A PRISMA Systematic Literature Review

References

Paradigm

My account