Data-driven modeling of process, structure and property in additive manufacturing: A review and future directions
暂无分享,去创建一个
Dazhong Wu | Lei Chen | Zhuo Wang | M. Banu | Pengwei Liu | Qingyang Liu | Wenhua Yang | Ying-ying Zhao
[1] R. Gao,et al. Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm , 2022, Journal of Manufacturing Systems.
[2] Bing Liu,et al. Residual thermal stress prediction for continuous tool-paths in wire-arc additive manufacturing: a three-level data-driven method , 2021, Virtual and Physical Prototyping.
[3] T. DebRoy,et al. Physics-informed machine learning and mechanistic modeling of additive manufacturing to reduce defects , 2021 .
[4] P. Michaleris,et al. A Physics-Informed Two-Level Machine Learning Model for Predicting Melt-Pool Size in Laser Powder Bed Fusion , 2021 .
[5] Qi Zhou,et al. Multi-objective process parameters optimization of SLM using the ensemble of metamodels , 2021 .
[6] Levent Burak Kara,et al. StressGAN: A Generative Deep Learning Model for Two-Dimensional Stress Distribution Prediction , 2021 .
[7] Lei Chen,et al. A gleeble-assisted study of phase evolution of Ti-6Al-4V induced by thermal cycles during additive manufacturing , 2021 .
[8] J. Tong,et al. Machine learning-based microstructure prediction during laser sintering of alumina , 2021, Scientific Reports.
[9] B. Rankouhi,et al. Compositional grading of a 316L-Cu multi-material part using machine learning for the determination of selective laser melting process parameters , 2021 .
[10] P. Withers,et al. A machine-learning fatigue life prediction approach of additively manufactured metals , 2021 .
[11] Yingfeng Zhang,et al. A big data-driven framework for sustainable and smart additive manufacturing , 2021, Robotics Comput. Integr. Manuf..
[12] 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.
[13] L. Bian,et al. Deep Learning-Based Data Fusion Method for In Situ Porosity Detection in Laser-Based Additive Manufacturing , 2020, Journal of Manufacturing Science and Engineering.
[14] Koji Fukagata,et al. CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers , 2020, Fluid Dynamics Research.
[15] C. Wang,et al. Machine learning in additive manufacturing: State-of-the-art and perspectives , 2020, Additive Manufacturing.
[16] E. Olivetti,et al. Data-driven materials research enabled by natural language processing and information extraction , 2020, Applied Physics Reviews.
[17] Guo Zhen,et al. Digital Twins for Additive Manufacturing: A State-of-the-Art Review , 2020, Applied Sciences.
[18] David W. Rosen,et al. Machine learning integrated design for additive manufacturing , 2020, Journal of Intelligent Manufacturing.
[19] Wenhua Yang,et al. Uncertainty quantification and reduction in metal additive manufacturing , 2020, npj Computational Materials.
[20] Xuxiao Li,et al. Modeling process–structure–property relationships in metal additive manufacturing: a review on physics-driven versus data-driven approaches , 2020, Journal of Physics: Materials.
[21] Dazhong Wu,et al. Prediction of melt pool temperature in directed energy deposition using machine learning , 2020 .
[22] U. Kühn,et al. Optimizing laser powder bed fusion of Ti-5Al-5V-5Mo-3Cr by artificial intelligence , 2020 .
[23] Kahraman G. Demir,et al. Machine Learning for Advanced Additive Manufacturing , 2020, Matter.
[24] S. Mahadevan,et al. Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication , 2020, JOM.
[25] Luis Javier Segura,et al. Unsupervised learning for the droplet evolution prediction and process dynamics understanding in inkjet printing , 2020 .
[26] 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.
[27] Navid Zobeiry,et al. A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications , 2020, Eng. Appl. Artif. Intell..
[28] J. Lifton,et al. Artificial neural network-based geometry compensation to improve the printing accuracy of selective laser melting fabricated sub-millimetre overhang trusses , 2020 .
[29] Adnan O. M. Abuassba,et al. Data augmentation in microscopic images for material data mining , 2020, npj Computational Materials.
[30] Zhimin Xi,et al. Calibration and Validation Framework for Selective Laser Melting Process Based on Multi-Fidelity Models and Limited Experiment Data , 2020 .
[31] Jinhui Yan,et al. Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks , 2020, Computational Mechanics.
[32] G. D. Goh,et al. A review on machine learning in 3D printing: applications, potential, and challenges , 2020, Artificial Intelligence Review.
[33] Ali P. Gordon,et al. Predicting Flexural Strength of Additively Manufactured Continuous Carbon Fiber-Reinforced Polymer Composites Using Machine Learning , 2020, J. Comput. Inf. Sci. Eng..
[34] Kai Fukami,et al. Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data , 2020, Physics of Fluids.
[35] Xun Xu,et al. Achieving better connections between deposited lines in additive manufacturing via machine learning. , 2020, Mathematical biosciences and engineering : MBE.
[36] Jing Zhang,et al. Machine Learning in Additive Manufacturing: A Review , 2020, JOM.
[37] Ashley D. Spear,et al. Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods , 2020, Computational Materials Science.
[38] Zhenghui Sha,et al. Data-Driven Predictive Modeling of Tensile Behavior of Parts Fabricated by Cooperative 3D Printing , 2020, J. Comput. Inf. Sci. Eng..
[39] Grace X. Gu,et al. Prediction of composite microstructure stress-strain curves using convolutional neural networks , 2020, Materials & Design.
[40] Koji Fukagata,et al. Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes , 2020, Theoretical and Computational Fluid Dynamics.
[41] Zhaodong Zhang,et al. Phase-field-model-based analysis of the effects of powder particle on porosities and densities in selective laser sintering additive manufacturing , 2020 .
[42] Y. Liu,et al. Integration of phase-field model and crystal plasticity for the prediction of process-structure-property relation of additively manufactured metallic materials , 2020 .
[43] Olga Wodo,et al. Data-driven modeling of thermal history in additive manufacturing , 2020, Additive Manufacturing.
[44] Hui Yang,et al. Deep Learning of Variant Geometry in Layerwise Imaging Profiles for Additive Manufacturing Quality Control , 2019, Journal of Manufacturing Science and Engineering.
[45] R. Jones,et al. Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model , 2019, Mach. Learn. Sci. Technol..
[46] Joeri Van Mierlo,et al. Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review , 2019, Renewable and Sustainable Energy Reviews.
[47] Dongwon Shin,et al. Data analytics approach for melt-pool geometries in metal additive manufacturing , 2019, Science and technology of advanced materials.
[48] Jing Zhang,et al. Process Design of Laser Powder Bed Fusion of Stainless Steel Using a Gaussian Process-Based Machine Learning Model , 2019, JOM.
[49] Mohammed Al Kindi,et al. Influence of Laser Processing Strategy and Remelting on Surface Structure and Porosity Development during Selective Laser Melting of a Metallic Material , 2019, Metallurgical and Materials Transactions A.
[50] S. Krishnamurty,et al. From Scan Strategy to Melt Pool Prediction: A Neighboring-Effect Modeling Method , 2019, J. Comput. Inf. Sci. Eng..
[51] Wing Kam Liu,et al. Data-Driven Microstructure and Microhardness Design in Additive Manufacturing Using a Self-Organizing Map , 2019, Engineering.
[52] Yong Li,et al. Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives , 2019, Engineering.
[53] Andreas M. Kaplan,et al. A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence , 2019, California Management Review.
[54] George-Christopher Vosniakos,et al. Real-time simulation for long paths in laser-based additive manufacturing: a machine learning approach , 2019, The International Journal of Advanced Manufacturing Technology.
[55] T. Bligaard,et al. Machine Learning for Computational Heterogeneous Catalysis , 2019, ChemCatChem.
[56] Zhen Hu,et al. A Data-Driven Approach for Process Optimization of Metallic Additive Manufacturing Under Uncertainty , 2019, Journal of Manufacturing Science and Engineering.
[57] Kai Fukami,et al. Nonlinear mode decomposition with convolutional neural networks for fluid dynamics , 2019, Journal of Fluid Mechanics.
[58] Sankaran Mahadevan,et al. Uncertainty Quantification in Metallic Additive Manufacturing Through Physics-Informed Data-Driven Modeling , 2019, JOM.
[59] Luning Sun,et al. Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data , 2019, Computer Methods in Applied Mechanics and Engineering.
[60] Zhixiong Li,et al. Prediction of surface roughness in extrusion-based additive manufacturing with machine learning , 2019, Robotics and Computer-Integrated Manufacturing.
[61] Karthik Duraisamy,et al. Prediction of aerodynamic flow fields using convolutional neural networks , 2019, Computational Mechanics.
[62] Petros Koumoutsakos,et al. Machine Learning for Fluid Mechanics , 2019, Annual Review of Fluid Mechanics.
[63] Pham Luu Trung Duong,et al. Data-Driven Design Space Exploration and Exploitation for Design for Additive Manufacturing , 2019, Journal of Mechanical Design.
[64] Frederic E. Bock,et al. A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics , 2019, Front. Mater..
[65] Robert X. Gao,et al. Deep learning-based tensile strength prediction in fused deposition modeling , 2019, Comput. Ind..
[66] Linkan Bian,et al. Deep Learning for Distortion Prediction in Laser-Based Additive Manufacturing using Big Data , 2019, Manufacturing Letters.
[67] Maarten V. de Hoop,et al. Machine learning for data-driven discovery in solid Earth geoscience , 2019, Science.
[68] Fei-Yue Wang,et al. A Learning-Based Framework for Error Compensation in 3D Printing , 2019, IEEE Transactions on Cybernetics.
[69] T. Mukherjee,et al. A digital twin for rapid qualification of 3D printed metallic components , 2019, Applied Materials Today.
[70] Mark F. Horstemeyer,et al. Insight into the mechanisms of columnar to equiaxed grain transition during metallic additive manufacturing , 2019, Additive Manufacturing.
[71] Paris Perdikaris,et al. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , 2019, J. Comput. Phys..
[72] Emilie J. Siochi,et al. Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing , 2019, Advanced Materials Technologies.
[73] Paris Perdikaris,et al. Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data , 2019, J. Comput. Phys..
[74] Y. Adachi,et al. Property prediction and properties-to-microstructure inverse analysis of steels by a machine-learning approach , 2019, Materials Science and Engineering: A.
[75] Arman Sabbaghi,et al. Model transfer across additive manufacturing processes via mean effect equivalence of lurking variables , 2018, The Annals of Applied Statistics.
[76] Jack Beuth,et al. A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process , 2018, Additive Manufacturing.
[77] Felix W. Baumann,et al. Trends of machine learning in additive manufacturing , 2018 .
[78] Nils Thuerey,et al. Deep Learning Methods for Reynolds-Averaged Navier–Stokes Simulations of Airfoil Flows , 2018, AIAA Journal.
[79] Alaa Elwany,et al. Uncertainty Propagation Analysis of Computational Models in Laser Powder Bed Fusion Additive Manufacturing Using Polynomial Chaos Expansions , 2018, Journal of Manufacturing Science and Engineering.
[80] Kornel Ehmann,et al. Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks , 2018, Manufacturing Letters.
[81] Levent Burak Kara,et al. Deep Learning for Stress Field Prediction Using Convolutional Neural Networks , 2018, J. Comput. Inf. Sci. Eng..
[82] Dazhong Wu,et al. Predictive modelling of surface roughness in fused deposition modelling using data fusion , 2018, Int. J. Prod. Res..
[83] Wei-keng Liao,et al. Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets , 2018 .
[84] Wolfgang Ludwig,et al. Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials , 2018, npj Computational Materials.
[85] Dazhong Wu,et al. Predictive Modeling of Droplet Formation Processes in Inkjet-Based Bioprinting , 2018, Journal of Manufacturing Science and Engineering.
[86] Qiang Huang,et al. Opportunities and challenges of quality engineering for additive manufacturing , 2018, Journal of Quality Technology.
[87] Dazhong Wu,et al. Deep learning for smart manufacturing: Methods and applications , 2018, Journal of Manufacturing Systems.
[88] Wei Chen,et al. Microstructural Materials Design Via Deep Adversarial Learning Methodology , 2018, Journal of Mechanical Design.
[89] Wei Chen,et al. A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions , 2018, Scientific Reports.
[90] Fang Liu,et al. Data Processing and Text Mining Technologies on Electronic Medical Records: A Review , 2018, Journal of healthcare engineering.
[91] J. S. Zuback,et al. Additive manufacturing of metallic components – Process, structure and properties , 2018 .
[92] Alessandra Caggiano,et al. Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning , 2018, Materials.
[93] Fugee Tsung,et al. A prediction and compensation scheme for in-plane shape deviation of additive manufacturing with information on process parameters , 2018 .
[94] Hang Z. Yu,et al. Integration of physically-based and data-driven approaches for thermal field prediction in additive manufacturing , 2018 .
[95] Shu Beng Tor,et al. Anisotropy and heterogeneity of microstructure and mechanical properties in metal additive manufacturing: A critical review , 2018 .
[96] Alaa Elwany,et al. Gaussian process-based surrogate modeling framework for process planning in laser powder-bed fusion additive manufacturing of 316L stainless steel , 2018 .
[97] E. Holm,et al. Applied machine learning to predict stress hotspots I: Face centered cubic materials , 2017, International Journal of Plasticity.
[98] Mohsen Attaran,et al. The rise of 3-D printing: The advantages of additive manufacturing over traditional manufacturing , 2017 .
[99] Abdulmecit Güldaş,et al. Experimental study on the 3D-printed plastic parts and predicting the mechanical properties using artificial neural networks , 2017 .
[100] Chiho Kim,et al. Machine Learning and Materials Informatics: Recent Applications and Prospects , 2017, 1707.07294.
[101] Arnulf Jentzen,et al. Solving high-dimensional partial differential equations using deep learning , 2017, Proceedings of the National Academy of Sciences.
[102] Xinyi Gong,et al. Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data , 2017, Integrating Materials and Manufacturing Innovation.
[103] Jack Beuth,et al. Prediction of lack-of-fusion porosity for powder bed fusion , 2017 .
[104] Akhil Garg,et al. Performance evaluation of warping characteristic of fused deposition modelling process , 2017 .
[105] Max Yi Ren,et al. Microstructure Representation and Reconstruction of Heterogeneous Materials via Deep Belief Network for Computational Material Design , 2016, ArXiv.
[106] S. Masood,et al. Analytical Modelling and Optimization of the Temperature-Dependent Dynamic Mechanical Properties of Fused Deposition Fabricated Parts Made of PC-ABS , 2016, Materials.
[107] Jacqueline M. Cole,et al. ChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature , 2016, J. Chem. Inf. Model..
[108] Alaa Elwany,et al. Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models , 2016 .
[109] Alexander M. Rubenchik,et al. Denudation of metal powder layers in laser powder bed fusion processes , 2016 .
[110] Sushant Negi,et al. Study on shrinkage behaviour of laser sintered PA 3200GF specimens using RSM and ANN , 2016 .
[111] Bryce Meredig,et al. Materials Data Infrastructure: A Case Study of the Citrination Platform to Examine Data Import, Storage, and Access , 2016 .
[112] Ryan R. Dehoff,et al. Numerical modeling of heat-transfer and the influence of process parameters on tailoring the grain morphology of IN718 in electron beam additive manufacturing ☆ , 2016 .
[113] Chandrika Kamath,et al. Data mining and statistical inference in selective laser melting , 2016, The International Journal of Advanced Manufacturing Technology.
[114] Klaus-Dieter Thoben,et al. Cloud-Based Automated Design and Additive Manufacturing: A Usage Data-Enabled Paradigm Shift , 2015, Sensors.
[115] H L Wei,et al. Evolution of solidification texture during additive manufacturing , 2015, Scientific Reports.
[116] Akhil Garg,et al. Measurement of environmental aspect of 3-D printing process using soft computing methods , 2015 .
[117] Moataz M. Attallah,et al. On the role of melt flow into the surface structure and porosity development during selective laser melting , 2015 .
[118] Surya R. Kalidindi,et al. Structure–property linkages using a data science approach: Application to a non-metallic inclusion/steel composite system , 2015 .
[119] Ryutaro Tanaka,et al. Permeability and strength of a porous metal structure fabricated by additive manufacturing , 2015 .
[120] Chandrika Kamath,et al. Observation of keyhole-mode laser melting in laser powder-bed fusion additive manufacturing , 2014 .
[121] O. Ojo,et al. Numerical modeling of microstructure evolution during laser additive manufacturing of a nickel-based superalloy , 2014 .
[122] Kang Tai,et al. State-of-the-art in empirical modelling of rapid prototyping processes , 2014 .
[123] C. Haden,et al. Progress Toward an Integration of Process–Structure–Property–Performance Models for “Three-Dimensional (3-D) Printing” of Titanium Alloys , 2014 .
[124] R. Paul,et al. Effect of Thermal Deformation on Part Errors in Metal Powder Based Additive Manufacturing Processes , 2014 .
[125] Kang Tai,et al. Formulation of bead width model of an SLM prototype using modified multi-gene genetic programming approach , 2014 .
[126] William E. Frazier,et al. Metal Additive Manufacturing: A Review , 2014, Journal of Materials Engineering and Performance.
[127] Wei Chen,et al. Descriptor-based methodology for statistical characterization and 3D reconstruction of microstructural materials , 2014 .
[128] Philip B. Prangnell,et al. Effect of build geometry on the β-grain structure and texture in additive manufacture of Ti6Al4V by selective electron beam melting , 2013 .
[129] Akhil Garg,et al. A hybrid \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{ M}5^\prime $$\end{document}-genetic programming approa , 2013, Journal of Intelligent Manufacturing.
[130] L. Deng,et al. The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web] , 2012, IEEE Signal Processing Magazine.
[131] Yaser Shanjani,et al. Characterizations of additive manufactured porous titanium implants. , 2012, Journal of biomedical materials research. Part B, Applied biomaterials.
[132] Kaufui Wong,et al. A Review of Additive Manufacturing , 2012 .
[133] Fahrettin Ozturk,et al. Flow curve prediction of Al-Mg alloys under warm forming conditions at various strain rates by ANN , 2011, Appl. Soft Comput..
[134] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[135] S. S. Al-Bermani,et al. The Origin of Microstructural Diversity, Texture, and Mechanical Properties in Electron Beam Melted Ti-6Al-4V , 2010 .
[136] Bingheng Lu,et al. The prediction of the building precision in the Laser Engineered Net Shaping process using advanced networks , 2010 .
[137] Xinhua Li,et al. ANN model for the prediction of density in Selective Laser Sintering , 2009, Int. J. Manuf. Res..
[138] Gordon Bell,et al. Beyond the Data Deluge , 2009, Science.
[139] J. T. Liu,et al. PREDICTION OF FLOW STRESS OF HIGH-SPEED STEEL DURING HOT DEFORMATION BY USING BP ARTIFICIAL NEURAL NETWORK , 2000 .
[140] Chua Chee Kai,et al. Rapid prototyping issues in the 21st century , 1999 .
[141] S. Hochreiter,et al. Long Short-Term Memory , 1997, Neural Computation.
[142] A. Wills,et al. Physics-Informed Machine , 2021 .
[143] 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.
[144] Angappa Gunasekaran,et al. Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications , 2019 .
[145] Qiang Huang,et al. Machine learning in tolerancing for additive manufacturing , 2018 .
[146] Bernd Markert,et al. Efficient numerical modeling of 3D-printed lattice-cell structures using neural networks , 2018 .
[147] K. Chou,et al. Phase-field simulation of microstructure evolution of Ti–6Al–4V in electron beam additive manufacturing process , 2016 .