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RESEARCH PAPER · Journal of Material Science and Technology · 2024

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

Hao Wang, Shang Gao, Boyi Wang, Yuntao Ma, Zhongkai Guo, Kai Zhang, K. Zhang, Y. Yang, Xiupan Yue, J. Hou, H.J. Huang, H.J. Huang, Gongquan Xu, S.J. Li, Aihan Feng, C.Y. Teng, Aijun J. Huang, Lai‐Chang Zhang, D.L. Chen

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Recent advances in machine learning-assisted fatigue life prediction of additive manufactured metallic materials: A reviewOriginalDeep Learning Methods Utilization in Mechanical Property of Medium‐Mn Steel15Evaluating fatigue onset in metallic materials: Problem, current focus and future perspectives77Advancing Metallurgical Practices: Predictive Modeling for Defect Detection using CNN-Random Forest Fusion0Review of progress in calculation and simulation of high-temperature oxidation181High-Cycle Fatigue Performance of Laser Powder Bed Fusion Ti-6Al-4V Alloy with Inherent Internal Defects: A Critical Literature Review30A stacking ensemble model for predicting the flexural fatigue life of fiber-reinforced concrete11Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading18Critical physics-informed fatigue life prediction of laser 3D printed AlSi10Mg alloys with mass internal defects54Physics-informed machine learning for loading history dependent fatigue delamination of composite laminates42A generalized machine learning framework to estimate fatigue life across materials with minimal data41Machine-learning synergy in high-entropy alloys: A review43Predicting crack nucleation in commercially pure titanium using orientation imaging microscopy and machine learning2Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review23Graphical Feature Construction-Based Deep Learning Model for Fatigue Life Prediction of AM Alloys14State-of-the-art review on various applications of machine learning techniques in materials science and engineering26Current Applications of Machine Learning in Additive Manufacturing: A Review on Challenges and Future Trends31A path-dependent adaptive physics-informed neural network for multiaxial fatigue life prediction40Damage tolerance performance of high strength and toughness titanium alloys formed by additive manufacturing in aerospace: A review16Fatigue Resistance in Engineering Components: A Comprehensive Review on the Role of Geometry and Its Optimization1Machine Learning-Based Methods for Materials Inverse Design: A Review34Properties of metals and nonmetals0New era towards autonomous additive manufacturing: a review of recent trends and future perspectives53Barriers to Adoption of Artificial Intelligence in Metal Additive Manufacturing0Fatigue failure mechanisms and life prediction of additive manufactured metallic lattices: a comprehensive review33Multi-objective optimization of laser powder bed fused titanium considering strength and ductility: A new framework based on explainable stacking ensemble learning and NSGA-II28Fatigue Short Crack Growth Prediction of Additively Manufactured Alloy Based on Ensemble Learning10A dual-purpose data-model interactive framework for multi-sensor selection and prognosis10Study of Hybrid Machine Learning Multiaxial Low‐Cycle Fatigue Life Prediction Model of CP‐Ti4Data-driven fatigue assessment of welded steel joints based on transfer learning6Machine learning enhanced metal 3D printing: high throughput optimization and material transfer extensibility24A TCN-based feature fusion framework for multiaxial fatigue life prediction: Bridging loading dynamics and material characteristics25Hybrid clustering-enhanced interpretable machine learning for fatigue life prediction across various cyclic stages in laser powder bed fused Ti-6Al-4V alloy11Vacuum-assisted HPDC lightweight Al-based entropy alloy with ultrahigh strength and exceptional thermal stability at elevated temperatures5Using Machine Learning Methods to Predict the Ductile-to-Brittle Transition Temperature Shift in RPV Steel Under Different Pulse Current Parameters3A novel fatigue life prediction method for the laser deposition repaired TA15 component with annealing heat treatment4Evolution behavior of competing fatigue failure and life prediction related to defect, stress and temperature for laser powder bed fused superalloy with solution aging treatment10From mechanism empirical to physics-informed: a comprehensive review of multiaxial non-proportional low-cycle fatigue life prediction3Rapid prediction of effective absorption bandwidth in PEEK/CF additive manufacturing metastructure via interpretable machine learning56Interpretable prediction of sample size–dependent fatigue crack formation lifetime using deep symbolic regression and polycrystalline plasticity models14Statistical analysis for the strength and thermal shock behavior of MoAlB3Enhancing Basketball Team Strategies Through Predictive Analytics of Player Performance6Forming process study of laser power bed fusion H13 steel by finite element simulation and experiment7A transfer learning enhanced physics-informed neural network for predicting fatigue life of megacasting alloy with limited data sizes21High-temperature experiments and simulation methods for oxidation behavior research of thermostructural materials11Fatigue damage mechanism and life prediction of cold expanded holes11Low-cycle fatigue life prediction of modified 9Cr-1Mo ferritic-martensitic steel in liquid lead–bismuth eutectic environments using a physically-based model4Multi-objective optimization of HVAF-sprayed Fe-based amorphous alloy coatings via machine learning for superior corrosion resistance23Machine Learning Approaches for Heat Treatment in Thermal Manufacturing Processes: A Review9Recent advances in machine learning-assisted design of additive manufacturing metastructures: a review55Systemic comparison of machine learning models in the optimization of flow field design for proton exchange membrane fuel cells24Fatigue Life Prediction and Feature Contribution Analysis of Surface-Strengthened Ti–6Al–4V Using a Preprocessing Neural Network2Comparison of methods for the determination of fatigue critical flaw size and implementation for probabilistic fatigue assessment7Achieving interpretable and efficient design of lightweight multi-principal element alloys via machine learning with optimized strengthening-toughening models14Machine learning assisted quality control in metal additive manufacturing: a review30An improved fatigue life prediction via defect-informed intelligent learning method23Microstructure evolution and fatigue properties of additive friction stir deposited Al-Mg-Si alloy17The Prediction of Cellular Materials Properties Through Deep Learning and Semantic Segmentation1Surface Modification of Porous Orthopedic Medical Implants Produced by Additive Manufacturing0Temperature compensation for electromechanical impedance-based damage detection of steel beams using neural network0Advances in Laser Powder Bed Fusion of Copper and Copper Alloys: Microstructure, Mechanical Behavior, and Conductivity10Effect of Sc and Zr additions on the microstructure, mechanical properties, and low-cycle fatigue behavior of Al-7Mg alloy during annealing5Machine learning-based prediction and optimization of performances of porous ZrO2/Al2O3 ceramics: a few-shot learning approach1An effective approach for identifying fatigue-critical defects from X-ray 3D reconstruction: Example in L-PBF AlSil0Mg alloys10Evolution of elastic anisotropy with temperature and orientation: Modelling, machine learning prediction and application for Nickel-Based Single-Crystal Superalloys0Fatigue Life Prediction of Different Materials Using Machine Learning1Temperature-controlled ultrasonic vibration assisted machining response and improvement mechanism for Ti2AlNb alloy manufactured by directed energy deposition1Interpretable high-throughput machine learning-based screening of high-thermal-conductivity composites with experimental validation6Artificial intelligence in metal additive manufacturing: current status, challenges, and future developments6Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation15Performance Evaluation of a Halbach Permanent Magnet Axial Protection Bearing Under Vertical Magnetic Levitation Flywheel Rotor Drop3Physical Equation for Fatigue Life Prediction of 2024-T3 Clad Al Alloy Assisted by Machine Learning1Bayesian model updating and model validation for fatigue life prediction of additively manufactured aluminum alloys2Exploring the constitutive description and workability of a Al0.1TiZrNbMoTa0.7 refractory high-entropy alloy during hot deformation using a novel SCSO-BP artificial neural network model10A novel semi-analytical sharp indentation model for inverse identifying metallic plastic properties4Intelligent strength prediction in structural ceramics enabled by statistical descriptors of inherent pores0Precision meets intelligence in AI-driven LPBF: transforming design, flexibility, quality, and sustainability in additive manufacturing2Fatigue strength prediction of additively manufactured Ti-6Al-4V: A defect-based physics-informed neural network2An XGboost enhanced physics-informed machine learning framework for multiaxial fatigue life prediction2Inverse design of electrical conductivity in AlSi8 alloy using Bayesian optimization1Machine learning applications in metallic materials: Recent advances and future perspectives8Machine Learning for Fatigue Life Prediction of Additively Manufactured Metals: A Review0Domain-adaptive dynamic prediction of remaining fatigue life based on in-situ spatiotemporal SEM sequences1Application of deep learning for predicting carbon segregation in large-diameter continuously cast steel billets0Interpretable corrosion rate prediction and rational design of amorphous alloy coatings: a deep learning framework with DenseLoss and SHAP interpretability0Data-Driven and Hybrid Modeling for Metal Fatigue: A Review of Classical Methods, Machine Learning, and Physics-Informed Neural Networks1Design of Cu–Ni–Si alloy with ultra-high strength and high conductivity based on machine learning model3Trends, Advancements in Manufacturing Field Using Machine Learning: A Hybrid Bibliometric Analysis on SCOPUS Database0An Efficient Multi-objective Evolutionary Learning Framework for Multi-Indicator Interval Prediction of Steel Properties in Continuous Annealing Process0Data-knowledge-driven enhanced stress field intensity approach for notch fatigue life prediction1AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials1Comprehensive unraveling of CT-derived defect characteristics in fused deposition modeling via machine learning: clustering, classification, regression and process correlation8Prediction of crack initiation and fatigue life of TSV under thermal cycling, vibration, and thermo‑vibrational coupled loading1Machine learning for materials analysis, process optimization, and defect detection in additive manufacturing: A review3Probabilistic fatigue assessment of a demonstrator manufactured by PBF-LB in Scalmalloy®1Fatigue Life Prediction and Fault Diagnosis Model for Mechanical Materials in Intelligent Manufacturing0Controlling element segregation during the solidification process of nickel-based superalloys using pulsed electric current1Predicting Fatigue Life of Additively Manufactured Metals Using Data‐Driven and Defect‐Based Models: a Comprehensive Review0A self-heating-based physics-guided neural network for fatigue life prediction of polymer matrix composites2A coupled theoretical and machine learning framework for fatigue crack growth prediction in steels0Interpretable machine learning for tensile strength prediction and processing optimization of thermoplastic vulcanizates0Mechanisms of Subsurface Crack Initiation and Microstructural Evolution in Aviation Bearing Steels Under Rolling Contact Fatigue: A Review0Prediction of grain boundary energy and understanding of grain boundary structure-energy relationship by machine learning0A large language model-based framework for fatigue life prediction of steels across spatiotemporal scales0Machine learning in laser powder bed fusion: a data-centric review of structural prediction and process optimization2Machine learning for fatigue life prediction of additively manufactured alloys: Quantifying predictive gain over physics baselines0Machine learning-assisted prediction of fatigue SN curves for laser powder bed fusion 316L stainless steel0Physics-informed attention-BiLSTM model for multiaxial fatigue life prediction under complex loading paths0

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