[1]CHEN F, ZHAN L, GAO T, et al. Creep Aging Properties Variation and Microstructure Evolution for 2195 Al-Li Alloys with Various Loading Rates[J]. Materials Science and Engineering:A, 2021, 827:142055.
[2]LI Y, SHI Z, LIN J, et al. A Unified Constitutive Model for Asymmetric Tension and Compression Creep-ageing Behaviour of Naturally Aged Al-Cu-Li Alloy[J]. International Journal of Plasticity, 2017, 89:130-49.
[3]WANG Y, ZHAO G, XU X, et al. Effect of Extrusion Parameters on Microstructure, Texture and Mechanical Property Anisotropy of Spray Deposited 2195 Al-Li Alloy Profile[J]. Materials Science and Engineering:A, 2021, 818:141406.
[4]WU C, LI H, BIAN T, et al. Non-isothermal Creep Age Forming of Al-Cu-Li Alloy:Experiment and Modelling[J]. Chinese Journal of Aeronautics, 2023, 36(5):566-81.
[5]ZHOU C, ZHAN L, LIU C, et al. Dislocation Density-mediated Creep Ageing Behavior of an Al-Cu-Li Alloy[J]. Journal of Materials Science & Technology, 2023,174:204-217.
[6]WU C H, LI H, LEI C, et al. Origin and Effect of Anisotropy in Creep Aging Behavior of Al-Cu-Li Alloy[J]. Journal of Materials Research and Technology, 2023, 26:3368-82.
[7]LIN Y C, JIANG Y Q, ZHANG X C, et al. Effect of Creep-aging Processing on Corrosion Resistance of an Al-Zn-Mg-Cu Alloy[J]. Materials & Design, 2014, 61:228-38.
[8]LIN J, HO K C, DEAN T A. An Integrated Process for Modelling of Precipitation Hardening and Springback in Creep Age-forming[J]. International Journal of Machine Tools and Manufacture, 2006, 46(11):1266-70.
[9]YANG Y, ZHAN L, MA Q, et al. Effect of Pre-Deformation on Creep Age Forming of AA2219 Plate:Springback, Microstructures and Mechanical Properties[J]. Journal of Materials Processing Technology, 2016, 229: 697-702.
[10]WANG K, ZHAN L H, YANG Y L, et al. Constitutive Modeling and Springback Prediction of Stress Relaxation Age Forming of Pre-deformed 2219 Aluminum Alloy[J]. Transactions of Nonferrous Metals Society of China, 2019, 29(6):1152-1160.
[11]MUHAMMAD W, BRAHME A P, IBRAGIMOVA O, et al. A Machine Learning Framework to Predict Local Strain Distribution and the Evolution of Plastic Anisotropy & Fracture in Additively Manufactured Alloys[J]. International Journal of Plasticity, 2021, 136:102867.
[12]FAZILY P, YOON J W. Machine Learning-driven Stress Integration Method for Anisotropic Plasticity in Sheet Metal Forming[J]. International Journal of Plasticity, 2023, 166:103642.
[13]IBRAGIMOVA O, BRAHME A, MUHAMMAD W, et al. A Convolutional Neural Network Based Crystal Plasticity Finite Element Framework to Predict Localised Deformation in Metals[J]. International Journal of Plasticity, 2022, 157:103374.
[14]HO K C, LIN J, DEAN T A. Constitutive Modelling of Primary Creep for Age Forming an Aluminium Alloy[J]. Journal of Materials Processing Technology, 2004, 153/154:122-127.
[15]BAK K Y, LEE W. Effect of Dimensionality on Convergence Rates of Kernel Ridge Regression Estimator[J]. Journal of Statistical Planning and Inference, 2024:106228(in press).
[16]NAOREM D, SINGH S R, SARMAH P. Improving Linear Orthogonal Mapping Based Cross-lingual Representation Using Ridge Regression and Graph Centrality[J]. Computer Speech & Language, 2024, 87:101640.
[17]ROUDAK M A, FARAHANI M, HOSSEINBEIGI F B. Extension of K-nearest Neighbors and Introduction of an Applicable Prediction Criterion for a Novel Monte Carlo Simulation-based Method in Structural Reliability[J]. Structures, 2024, 66:106867.
[18]LIU Q, MA J, ZHAO X, et al. A Novel Method for Fault Diagnosis and Type Identification of Cell Voltage Inconsistency in Electric Vehicles Using Weighted Euclidean Distance Evaluation and Statistical Analysis[J]. Energy, 2024, 293:130575.
[19]GIFFORD M, BAYRAK T. A Predictive Analytics Model for Forecasting Outcomes in the National Football League Games Using Decision Tree and Logistic Regression[J]. Decision Analytics Journal, 2023, 8:100296.
[20]ZHOU R, ZHANG Y, WANG Q, et al. A Hybrid Self-adaptive DWT-Wavenet-LSTM Deep Learning Architecture for Karst Spring Forecasting[J]. Journal of Hydrology, 2024, 634:131128.
[21]JERSE G, MARCUCCI A. Deep Learning LSTM-based Approaches for 10.7 cm Solar Radio Flux Forecasting Up to 45-days[J]. Astronomy and Computing, 2024, 46:100786.
[22]LIU X, XU P, ZHAO J, et al. Material Machine Learning for Alloys:Applications, Challenges and Perspectives[J]. Journal of Alloys and Compounds, 2022, 921:165984.
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