The potency of defects on fatigue of additively manufactured metals

[1]  Atilla Incecik,et al.  Ensemble learning for remaining fatigue life prediction of structures with stochastic parameters: A data-driven approach , 2022, Applied Mathematical Modelling.

[2]  N. Shamsaei,et al.  Structural integrity of additively manufactured aluminum alloys: Effects of build orientation on microstructure, porosity, and fatigue behavior , 2021, Additive Manufacturing.

[3]  M. Gorji,et al.  Machine learning predicts fretting and fatigue key mechanical properties , 2021, International Journal of Mechanical Sciences.

[4]  Haris Moazam Sheikh,et al.  Strength through defects: A novel Bayesian approach for the optimization of architected materials , 2021, Science advances.

[5]  R. P. Swamy,et al.  Fatigue life prediction of glass fiber reinforced epoxy composites using artificial neural networks , 2021, Composites Communications.

[6]  K. A. Padmanabhan,et al.  A phenomenological model for predicting long-term high temperature creep life of materials from short-term high temperature creep test data , 2021, International Journal of Mechanical Sciences.

[7]  Fu-Zhen Xuan,et al.  A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions , 2021, International Journal of Fatigue.

[8]  Yueling Guo,et al.  Improving mechanical properties of wire arc additively manufactured AA2196 Al–Li alloy by controlling solidification defects , 2021, Additive Manufacturing.

[9]  P. Withers,et al.  The effect of defect population on anisotropic fatigue resistance of selective laser melted AlSi10Mg alloy , 2021 .

[10]  Yanan Hu,et al.  Fatigue life evaluation of Ti–6Al–4V welded joints manufactured by electron beam melting , 2021 .

[11]  Wing Kam Liu,et al.  Image-based multiscale modeling with spatially varying microstructures from experiments: Demonstration with additively manufactured metal in fatigue and fracture , 2021 .

[12]  Y. Murakami,et al.  Essential structure of S-N curve: Prediction of fatigue life and fatigue limit of defective materials and nature of scatter , 2021 .

[13]  M. Buehler,et al.  Deep learning model to predict fracture mechanisms of graphene , 2021, npj 2D Materials and Applications.

[14]  H. Akebono,et al.  Machine Learning-Based Predictions of Fatigue Life and Fatigue Limit for Steels , 2021 .

[15]  P. Withers,et al.  In-situ synchrotron X-ray tomography investigation of damage mechanism of an extruded magnesium alloy in uniaxial low-cycle fatigue with ratchetting , 2021 .

[16]  J. Durodola.  Machine learning for design, phase transformation and mechanical properties of alloys , 2021 .

[17]  Yongming Liu,et al.  Fatigue property prediction of additively manufactured Ti-6Al-4V using probabilistic physics-guided learning , 2021 .

[18]  M. Wiercigroch,et al.  Analytical solution for circular inhomogeneous inclusion problems with non-uniform axisymmetric eigenstrain distribution , 2021 .

[19]  Hua Li,et al.  A machine learning guided investigation of quality repeatability in metal laser powder bed fusion additive manufacturing , 2021 .

[20]  P. Withers,et al.  A machine-learning fatigue life prediction approach of additively manufactured metals , 2021 .

[21]  X. Qi,et al.  Pore-affected fatigue life scattering and prediction of additively manufactured Inconel 718: An investigation based on miniature specimen testing and machine learning approach , 2021 .

[22]  Sen Liu,et al.  A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing , 2021, The International Journal of Advanced Manufacturing Technology.

[23]  E. Maire,et al.  Influence on microstructure, strength and ductility of build platform temperature during laser powder bed fusion of AlSi10Mg , 2020 .

[24]  Jie Chen,et al.  Probabilistic physics-guided machine learning for fatigue data analysis , 2020, Expert Syst. Appl..

[25]  Qingyuan Wang,et al.  Effects of defects on tensile and fatigue behaviors of selective laser melted titanium alloy in very high cycle regime , 2020 .

[26]  Liyang Xie,et al.  Determination of the fatigue P-S-N curves – A critical review and improved backward statistical inference method , 2020 .

[27]  Zhongxiao Peng,et al.  Machine-Learning Assisted Laser Powder Bed Fusion Process Optimization for AlSi10mg: New Microstructure Description Indices and Fracture Mechanisms , 2020, Acta Materialia.

[28]  A. Fatemi,et al.  Defects in additive manufactured metals and their effect on fatigue performance: A state-of-the-art review , 2020 .

[29]  F. Berto,et al.  Defects as a root cause of fatigue weakening of additively manufactured AlSi10Mg components , 2020 .

[30]  P. Withers,et al.  A new approach to correlate the defect population with the fatigue life of selective laser melted Ti-6Al-4V alloy , 2020 .

[31]  P. Withers,et al.  The effect of manufacturing defects on the fatigue life of selective laser melted Ti-6Al-4V structures , 2020 .

[32]  Shun-Peng Zhu,et al.  Novel probabilistic model for searching most probable point in structural reliability analysis , 2020 .

[33]  Y. Ono,et al.  Tensile properties prediction by multiple linear regression analysis for selective laser melted and post heat-treated Ti-6Al-4V with microstructural quantification , 2020, Materials Science and Engineering: A.

[34]  Fabian Birzele,et al.  An Introduction to Machine Learning , 2020, Clinical pharmacology and therapeutics.

[35]  S. Anand,et al.  Prediction of selective laser melting part quality using hybrid Bayesian network , 2020 .

[36]  Meng Zhang,et al.  High cycle fatigue life prediction of laser additive manufactured stainless steel: A machine learning approach , 2019, International Journal of Fatigue.

[37]  Jing Zhang,et al.  Process Design of Laser Powder Bed Fusion of Stainless Steel Using a Gaussian Process-Based Machine Learning Model , 2019, JOM.

[38]  K. Chan,et al.  A Methodology for Predicting Surface Crack Nucleation in Additively Manufactured Metallic Components , 2019, Metallurgical and Materials Transactions A.

[39]  Abdullah Al Mamun,et al.  Interrupted fatigue testing with periodic tomography to monitor porosity defects in wire + arc additive manufactured Ti-6Al-4V , 2019, Additive Manufacturing.

[40]  Thomas R. Kurfess,et al.  Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images , 2018, Journal of Intelligent Manufacturing.

[41]  Huaizhi Su,et al.  An approach using random forest intelligent algorithm to construct a monitoring model for dam safety , 2019, Engineering with Computers.

[42]  Deok-Kee Choi,et al.  Data-Driven Materials Modeling with XGBoost Algorithm and Statistical Inference Analysis for Prediction of Fatigue Strength of Steels , 2019, International Journal of Precision Engineering and Manufacturing.

[43]  G. Meneghetti,et al.  An analysis of defects influence on axial fatigue strength of maraging steel specimens produced by additive manufacturing , 2019, International Journal of Fatigue.

[44]  Emilie Beevers,et al.  Fatigue properties and material characteristics of additively manufactured AlSi10Mg – Effect of the contour parameter on the microstructure, density, residual stress, roughness and mechanical properties , 2018, International Journal of Fatigue.

[45]  Moataz M. Attallah,et al.  Linking microstructure and processing defects to mechanical properties of selectively laser melted AlSi10Mg alloy , 2018, Theoretical and Applied Fracture Mechanics.

[46]  Ashok Samal,et al.  Defect Detection and Monitoring in Metal Additive Manufactured Parts through Deep Learning of Spatially Resolved Acoustic Spectroscopy Signals , 2018, Smart and Sustainable Manufacturing Systems.

[47]  Fu-Zhen Xuan,et al.  Fatigue life and mechanistic modeling of interior micro-defect induced cracking in high cycle and very high cycle regimes , 2018, Acta Materialia.

[48]  J. Newman,et al.  Fatigue life prediction of additively manufactured material: Effects of surface roughness, defect size, and shape , 2018 .

[49]  W. Ludwig,et al.  Predicting the 3D fatigue crack growth rate of small cracks using multimodal data via Bayesian networks: In-situ experiments and crystal plasticity simulations , 2018, Journal of the Mechanics and Physics of Solids.

[50]  P J Withers,et al.  The Influence of Porosity on Fatigue Crack Initiation in Additively Manufactured Titanium Components , 2017, Scientific Reports.

[51]  Fatih Porikli,et al.  A Unified Approach for Conventional Zero-Shot, Generalized Zero-Shot, and Few-Shot Learning , 2017, IEEE Transactions on Image Processing.

[52]  Deyu Meng,et al.  Few-Example Object Detection with Model Communication , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[53]  Alaa Elwany,et al.  Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models , 2016 .

[54]  P. Prangnell,et al.  Porosity Regrowth During Heat Treatment of Hot Isostatically Pressed Additively Manufactured Titanium Components , 2016 .

[55]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[56]  Iain Todd,et al.  XCT analysis of the influence of melt strategies on defect population in Ti?6Al?4V components manufactured by Selective Electron Beam Melting , 2015 .

[57]  Sanguthevar Rajasekaran,et al.  Accelerating materials property predictions using machine learning , 2013, Scientific Reports.

[58]  Yukitaka Murakami,et al.  Material defects as the basis of fatigue design , 2012 .

[59]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[60]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[61]  Jean-Michel Poggi,et al.  Variable selection using random forests , 2010, Pattern Recognit. Lett..

[62]  Paola Annoni,et al.  Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index , 2010, Comput. Phys. Commun..

[63]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[64]  Marek Słoński,et al.  Application of the Gaussian process for fatigue life prediction under multiaxial loading , 2022, Mechanical Systems and Signal Processing.

[65]  W. Muhammad,et al.  A machine learning framework to predict local strain distribution and the evolution of plastic anisotropy & fracture in additively manufactured alloys , 2021, International Journal of Plasticity.

[66]  Yanjun Chen,et al.  A rolling bearing fault diagnosis method using novel lightweight neural network , 2021, Measurement Science and Technology.

[67]  Weihong Zhang,et al.  Effects of build direction on tensile and fatigue performance of selective laser melting Ti6Al4V titanium alloy , 2020 .

[68]  Robert X. Gao,et al.  Machine learning-based image processing for on-line defect recognition in additive manufacturing , 2019, CIRP Annals.

[69]  Zhengguo Xu,et al.  A Data-Driven Health Prognostics Approach for Steam Turbines Based on Xgboost and DTW , 2019, IEEE Access.

[70]  N. Hrabe,et al.  Fatigue properties of a titanium alloy (Ti–6Al–4V) fabricated via electron beam melting (EBM): Effects of internal defects and residual stress , 2017 .

[71]  L. Breiman.  Random Forests , 2001, Machine Learning.