A novel approach based on the elastoplastic fatigue damage and machine learning models for life prediction of aerospace alloy parts fabricated by additive manufacturing

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DebRoy, T., Wei, H. L., Zuback, J. S., Mukherjee, T., Elmer, J. W., Milewski, J. O., Beese, A. M., Wilson-Heid, A., De, A., & Zhang, W. (2018) · Progress in Materials Science · doi:10.1016/j.pmatsci.2017.10.001 · Cited by 8,805

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Mower, T. M., & Long, M. (2015) · Materials Science and Engineering A · doi:10.1016/j.msea.2015.10.068 · Cited by 1,013

On some modifications of kinematic hardening to improve the description of ratchetting effects

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Development of Predictive Maintenance System for Haemodialysis Reverse Osmosis Water Purification System

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Powder Bed Additive Manufacturing Using Machine Learning Algorithms for Multidisciplinary Applications: A Review and Outlook

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Automatic high-precision fatigue crack tip localization without empirical parameters in enormous DIC datasets of GH4169 superalloy served under extreme environments

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Locally assembled stiffness matrix: a novel method to obtain global stiffness matrix

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Prediction of the natural frequencies of various beams using regression machine learning models

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Review: Recent progress in titanium alloys development via machine learning and high-throughput preparation

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Physics-Informed Neural Networks for Multiaxial Fatigue Life Prediction of Aluminum Alloy

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Genetic algorithm-enhanced hybrid physics-informed neural networks for very high cycle fatigue life prediction

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Fatigue life prediction of ultrasonic impact treatment aluminum alloy weld joint based on the continuous damage model and artificial neural network

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Xie, P., Deqiao, 谢. X., Kai, 周. Z., Lida, 沈. S., Zongjun, 田. T., & Jianfeng, 赵. Z. (2024) · Laser & Optoelectronics Progress · doi:10.3788/lop240432 · Cited by 4

A novel multiaxial fatigue life prediction model based on the critical plane theory and machine-learning method

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Predicting the low‐cycle fatigue life of Ti‐6Al‐4V alloy using backpropagation neural network optimized by the improved dung beetle algorithm

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Low cycle fatigue life prediction for neutron-irradiated and nonirradiated RAFM steels via machine learning

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Ratcheting–Fatigue Damage Assessment of Additively Manufactured SS304L and AlSi10Mg Samples under Asymmetric Stress Cycles

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Transfer-Learning Prediction Model for Low-Cycle Fatigue Life of Bimetallic Steel Bars

Xue, X., Wang, F., Wang, N., Hua, J., & Deng, W. (2024) · Buildings · doi:10.3390/buildings14082275 · Cited by 4

Prediction of Rubber Fatigue Life Using an Assimilation‐based Learning Approach and Incremental Crack Propagation Model

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

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Cross-validated neural network models for fretting fatigue life prediction across multiple materials and contact geometries

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Prediction of Fatigue Life of Materials Using Machine Learning Approach: A Review

Tefera, G. A., & Koricho, E. G. (2024) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-031-64151-0_11 · Cited by 2

A Hybrid Optimization Algorithm for a Multi-Objective Aircraft Loading Problem With Complex Constraints

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Chike, O. G., Chin, Y. J., Ahmad, N. B., & Ali, W. F. F. W. (2025) · Operations Research Forum · doi:10.1007/s43069-025-00440-1 · Cited by 2

Transforming Aerospace Design and Manufacturing Through Machine Learning

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Artificial intelligence for materials damage diagnostics and prognostics

Malik, S., & Kontsos, A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99134-6.00004-9 · Cited by 1

Uncertainty quantification of local elastic properties in additively manufactured materials for topology optimization applications using machine learning

Kazemi, Z., & Steeves, C. A. (2025) · Acta Mechanica · doi:10.1007/s00707-025-04371-0 · Cited by 2

From precision to prediction in probabilistic FEA fatigue for structural reliability

Barah, O. O. (2025) · Engineering Research Express · doi:10.1088/2631-8695/ae31cc · Cited by 1

A multiaxial fatigue life prediction method using a hybrid GRU-FCNN model optimized by grid search

Yao, J., Song, Y., Guo, R., Li, X., Fu, L., & Yu, X. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.109581 · Cited by 1

Domain-adaptive dynamic prediction of remaining fatigue life based on in-situ spatiotemporal SEM sequences

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Data-Driven and Hybrid Modeling for Metal Fatigue: A Review of Classical Methods, Machine Learning, and Physics-Informed Neural Networks

Shi, Y., Dey, A. S., & Qin, Y. (2026) · Metals · doi:10.3390/met16050476 · Cited by 1

Prediction of hydrogen-related low-cycle fatigue life of stainless steel based on physics-guided and interpretable machine learning

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Probabilistic Fatigue Life Prediction of AM-Built Alloy Using Physics-Guided Neural Network

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A Predictive Model of Dimensional Deviation Based on Regeneration PSO-SVR with Cutting Feature Weight in Milling

Yao, H., Luo, B., Li, J., Zhang, K., & Cao, Z. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2101/1/012001 · Cited by 1

A Machine Learning Model for Predicting Progressive Crack Extension based on DCPD Fatigue DataPreprint

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ANALYSIS OF CORD–RUBBER COMPOSITE RESPONSE BY USING THE NONLINEAR CONTINUUM DAMAGE MECHANICS APPROACH

Assaad, M. C., Du, M., & Ebbott, T. (2023) · Rubber Chemistry and Technology · doi:10.5254/rct-23.361055

Study on the Potential of New Load-Carrying Capacity Descriptions for the Service Life Calculations of Gears

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Res-GCM: A Damage Trend Prediction Method for Aero-Engines Based on Res-Graph Convolutional Markov Chains

Wang, Z., Fu, X., & Zhang, J. (2024) · International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) · doi:10.1109/sdpc62810.2024.10707736

Fatigue Life Prediction of Ultrasonic Impact Treatment Aluminum Alloy Welded Joints Based on the Continuous Damage Model and Artificial Neural NetworkPreprint

Cong, J., Liu, Z., Zhou, S., Zhu, X., & Gao, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5226341

Developing a deep learning framework for predicting the mechanical properties of AM particulate composites

Ganapathy, V., Venkatesh, R. P., Manoharan, S., & Anand, P. (2025) · Progress in Additive Manufacturing · doi:10.1007/s40964-025-01338-4

A Comparative Analysis of Financial Risk Management Strategies: Evaluating the Effectiveness of a Novel Approach Against Traditional Models

Meesaala, K. M., Varalakshmi, S., Sharma, R. V., Sanadi, H., Begum, S., & Bhutani, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-03072-6_35

Introduction

Kang, G., Kan, Q., Zhang, X., Hu, Y.-N., & Li, X. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44615-3.00002-8

Machine learning–based fatigue life prediction of solid materials

Kang, G., Kan, Q., Zhang, X., Hu, Y.-N., & Li, X. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44615-3.00003-x

Interpretable machine learning-based framework for predicting surface defect-induced fatigue behavior in additively manufactured Ti6Al4V miniature components

Hou, Y., Wauters, T., Liu, J., Liu, Q., Zhou, X., & Talemi, R. (2026) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2026.111959

Cyclic deformation-induced stabilization and damping mechanisms in Aluminum nanofoam; A molecular dynamics study

Sameti, A. R., Sefti, M. V., & Khoei, A. R. (2026) · Acta Materialia · doi:10.1016/j.actamat.2026.122157

Correlation-guided temporal deep learning model for the prediction of fatigue life of printed AlSi10Mg alloy

Kumar, A., Parui, A., Inani, H., Bag, S., & Srivastava, V. (2026) · Proceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and Applications · doi:10.1177/14644207261434131

Integration of Micro-XCT and machine learning for defect-driven fatigue life prediction in DED-Arc titanium alloys

Yu, B., Wang, P., Zhao, P., Wang, Y., Song, X., & Qian, H. (2026) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2026.112335

Fatigue failure of additively manufactured components: a comprehensive review on influencing factors

Subbiah, M., Rajamohan, V., & Santhanam, R. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae7ce4

In Situ Investigation on the Roles of Microstructural Characteristics in Fatigue Anisotropy and Crack Propagation of LPBF–IN718 Alloy

Shi, Z., Hu, X., Li, Z., He, X., Chen, Z., Xie, B., Zhuo, Z., Yu, R., Lu, H., & Zhu, Q. (2026) · Rare Metals · doi:10.1002/rar2.70415

A novel XAI software based on finite repeated game theory for interpreting machine learning modelsPreprint

Renzo, D. A., Laurenti, M., Foti, P., Benedetti, M., Tirillò, J., & Berto, F. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7167981

A unified mechanics theory framework for fully reversed high cycle fatigue of FCC aluminum based on two-scale microplasticity entropy production

Lee, H. W. (2026) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2026.109957

Neural network for predicting fatigue life of metallic components via multi-feature fusion

Pan, Z., Xu, F., Chen, C., & Qian, X. (2026) · Journal of Constructional Steel Research · doi:10.1016/j.jcsr.2026.110659

A New Fatigue Damage Framework Incorporating Process‐Induced Residual Stress for Fatigue Life Prediction of SLM‐Fabricated AlSi10Mg

Li, Z., Wang, X., Su, S., He, X., Hu, W., Meng, Q., & Zhan, Z. (2026) · Fatigue & Fracture of Engineering Materials & Structures · doi:10.1111/ffe.70447

Artificial Intelligence Techniques for Fatigue Performance Prediction in Metal Additive Manufacturing: A Systematic Review

Tushar, S. R., Khonsari, M., & Bappy, M. M. (2026) · Manufacturing Letters · doi:10.1016/j.mfglet.2026.08.024

Experimental evaluation of PVB/ITO nanocomposite sensors for bending crack monitoring in metallic structures

Almeida, A. D. C., Preto, E., Ambrozini, B., Gonçalves, P. J. P., da Silva, S., & Orlandi, M. O. (2026) · Journal of Intelligent Material Systems and Structures · doi:10.1177/1045389x261491149

Machine learning-based prediction of temperature dependent thermal conductivity in titanium alloys utilizing compositional, microstructural phase, and thermal processing parameters

Farazi, K. A., Fahim, K. S. M. J., Shafique, S., & Islam, M. A. (2026) · Materials Today Communications · doi:10.1016/j.mtcomm.2026.116498

Contract for testing AM aerospace parts extended

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Fatigue-life prediction of additively manufactured metals by continuous damage mechanics (CDM)-informed machine learning with sensitive features

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Data-driven fatigue life prediction in additive manufactured titanium alloy: A damage mechanics based machine learning framework

Zhan, Z., Hu, W., & Meng, Q. (2021) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2021.107850 · Cited by 158

Recent advances in machine learning-assisted fatigue life prediction of additive manufactured metallic materials: A review

Wang, H., Gao, S. L., Wang, B., Ma, Y., Guo, Z., Zhang, K., Yang, Y., Yue, X., Hou, J., Huang, H. J., et al. (2024) · Journal of Material Science and Technology · doi:10.1016/j.jmst.2024.01.086 · Cited by 122