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RESEARCH PAPER · Additive manufacturing · 2020

Invited review: Machine learning for materials developments in metals additive manufacturing

Nathan Scott Johnson, Praveen S. Vulimiri, Albert C. To, Xin-Ya Zhang, Craig Brice, Branden B. Kappes, Aaron P. Stebner

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Invited review: Machine learning for materials developments in metals additive manufacturingOriginalToward a common laser powder bed fusion qualification test artifact46Current state and future trends in laser powder bed fusion technology14Efficiency and Accuracy of Simulated Microstructure Images Generated by Machine Learning1ICT material development processes for e-learning1Multiphysics Modeling and Numerical Simulation in Computer-Aided Manufacturing Processes22Kietųjų dalelių taršos galandinimo proceso metu tyrimas0A mesoscopic digital twin that bridges length and time scales for control of additively manufactured metal microstructures35A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions247A machine learning method for defect detection and visualization in selective laser sintering based on convolutional neural networks233Comprehensive Quality Investigations of Wire-feed Additive Manufacturing based on Machine Learning of Experimental Data2Comprehensive Quality Investigations of Wire-feed Additive Manufacturing by Learning of Experimental Data0Thermal field prediction in DED manufacturing process using Artificial Neural Network17Recent progress and scientific challenges in multi-material additive manufacturing via laser-based powder bed fusion316Hydroxylapatite and Related Minerals in Bone and Dental Tissues: Structural, Spectroscopic and Mechanical Properties from a Computational Perspective48A New Concept for Modeling Phase Transformations in Ti6Al4V Alloy Manufactured by Directed Energy Deposition14Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing154Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing143The case for digital twins in metal additive manufacturing91Machine learning in predicting mechanical behavior of additively manufactured parts200Data‐Driven Approaches Toward Smarter Additive Manufacturing37Towards Automatic Detection of Precipitates in Inconel 625 Superalloy Additively Manufactured by the L-PBF Method5Disadvantage1Modelling of Microstructure Formation in Metal Additive Manufacturing: Recent Progress, Research Gaps and Perspectives30A review on voids of 3D printed parts by fused filament fabrication443Prediction of solidification microstructure of titanium aluminum intermetallic alloy by laser surface remelting23Machine learning for metal additive manufacturing: Towards a physics-informed data-driven paradigm309Correlating in-situ sensor data to defect locations and part quality for additively manufactured parts using machine learning66COMPUTATIONAL MODELING AND PHYSICS-INFORMED MACHINE LEARNING OF METAL ADDITIVE MANUFACTURING: STATE-OF-THE-ART AND FUTURE PERSPECTIVE8Future perspectives0Materials Selection in Product Development: Challenges and Quality Management Tools8Defects and anomalies in powder bed fusion metal additive manufacturing491A fatigue life prediction approach for laser-directed energy deposition titanium alloys by using support vector regression based on pore-induced failures90Machine learning-enabled prediction of density and defects in additively manufactured Inconel 718 alloy25Data Centric Design: A New Approach to Design of Microstructural Material Systems52Research and application of machine learning for additive manufacturing386Detection, classification and prediction of internal defects from surface morphology data of metal parts fabricated by powder bed fusion type additive manufacturing using an electron beam64Laser directed energy deposition of AISI 316L stainless steel: The effect of build direction on mechanical properties in as-built and heat-treated conditions61CNN-BiLSTM enabled prediction on molten pool width for thin-walled part fabrication using Laser Directed Energy Deposition54Colloidal Processing of Complex-Shaped ZrB2-Based Ultra-High-Temperature Ceramics: Progress and Prospects18ICME framework to simulate microstructure evolution during laser powder bed fusion of Haynes 282 nickel-based superalloy23Computational Acceleration of Topology Optimization Using Parallel Computing and Machine Learning Methods – Analysis of Research Trends37MeltpoolNet: Melt pool characteristic prediction in Metal Additive Manufacturing using machine learning151When AI meets additive manufacturing: Challenges and emerging opportunities for human-centered products development106The interplay between vapour, liquid, and solid phases in laser powder bed fusion143Polishing of additive manufactured metallic components: retrospect on existing methods and future prospects69A Systematic Literature Review of Machine Learning Approaches for Optimization in Additive Manufacturing7Interrelated process-geometry-microstructure relationships for wire-feed laser additive manufacturing22Achieving optimal process design for minimizing porosity in additive manufacturing of Inconel 718 using a deep learning-based pore detection approach19Optimization of Laser‐Powder Bed Fusion Processed Fe–4.5Si Alloy via Response Surface Methodology6Predicting defects in laser powder bed fusion using in-situ thermal imaging data and machine learning65State-of-the-Art Review of Machine Learning Applications in Additive Manufacturing; from Design to Manufacturing and Property Control44Incorporation of machine learning in additive manufacturing: a review39A universal predictor-based machine learning model for optimal process maps in laser powder bed fusion process40Multifidelity Physics-Constrained Neural Networks With Minimax Architecture21Effect of microstructural heterogeneity on fatigue strength predicted by reinforcement machine learning22A review of machine learning techniques for process and performance optimization in laser beam powder bed fusion additive manufacturing110Compressive behavior of Co-Cr-Mo radially graded porous structures under as-built and heat-treated conditions14Unified fatigue life modelling and uncertainty estimation of Ni-based superalloy family with a supervised machine learning approach39A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management70Systematic deep transfer learning method based on a small image dataset for spaghetti-shape defect monitoring of fused deposition modeling49An overview of modern metal additive manufacturing technology549Machine learning for intelligent welding and manufacturing systems: research progress and perspective review37Recent developments in the application of machine-learning towards accelerated predictive multiscale design and additive manufacturing133Multiaxial fatigue prediction and uncertainty quantification based on back propagation neural network and Gaussian process regression55Explainable machine learning for understanding and predicting geometry and defect types in Fe-Ni alloys fabricated by laser metal deposition additive manufacturing74Anomaly detection in laser powder bed fusion using machine learning: A review63Reduction of Material Usage in 3D Printable Structures Using Topology Optimization Accelerated with U-Net Convolutional Neural Network4Review of transfer learning in modeling additive manufacturing processes78Angle and direction effect on the performance of manual music box with PLA-base manufacturing using 3D printing1Computational and Data-Driven Materials Engineering: Multimodal Materials Datasets, Integration Frameworks, and Discovery Potential0FE² Computations With Deep Neural Networks: Algorithmic Structure, Data Generation, and Implementation3Feature Engineering in Additive Manufacturing5Introduction0Metal AM process-structure-property relational linkages using Gaussian process surrogates42Rapid alloying in additive manufacturing using integrated computational materials engineering3Self-Supervised Representation Learning of Microstructures from Electron Microscopy for Property Prediction0Stability in Reinforcement Learning Process Control for Additive Manufacturing9In situ growth of carbon nanotubes on NiTi powder for printing high-performance NiTi matrix composite5Additively manufactured materials and structures: A state-of-the-art review on their mechanical characteristics and energy absorption440Development of macro-defect-free PBF-EB-processed Ti–6Al–4V alloys with superior plasticity using PREP-synthesized powder and machine learning-assisted process optimization28Research and application of artificial intelligence techniques for wire arc additive manufacturing: a state-of-the-art review144Analysis and evaluation of machine learning applications in materials design and discovery33Perspective: Machine learning in experimental solid mechanics68A survey of machine learning in additive manufacturing technologies72Multiphysics multi-scale computational framework for linking process–structure–property relationships in metal additive manufacturing: a critical review74Predictions of Additive Manufacturing Process Parameters and Molten Pool Dimensions with a Physics-Informed Deep Learning Model72Machine Learning Applications for 3D-Printed Polymers and Their Composites6Evaluating the Predictability of Surface Roughness of Ti–6Al–4V Alloy from Selective Laser Melting15Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects109Towards the next generation of machine learning models in additive manufacturing: A review of process dependent material evolution124Using deep learning for the prediction of mixing patterns in two component-colored solutions as a proxy to dispersion in nanocomposite coatings8Subdivisional modelling method for matched metal additive manufacturing and its implementation on novel negative Poisson's ratio lattice structures21Machine learning-based fatigue life prediction of metal materials: Perspectives of physics-informed and data-driven hybrid methods281A Conceptual Framework Towards the Realization of In situ Monitoring and Control of End-to-End Additive Manufacturing Process1A Hermite Surface Triangle Modeling Method Considering High-Precision Fitting of 3D Printing Models4Holistic computational design within additive manufacturing through topology optimization combined with multiphysics multi-scale materials and process modelling128Efficient prediction of thermal history in wire and arc additive manufacturing combining machine learning and numerical simulation33Challenges and Opportunities for Machine Learning in Multiscale Computational Modeling37A comparative study of machine learning algorithms in the prediction of bead geometry in wire-arc additive manufacturing47A review of artificial intelligence applications in manufacturing operations250A novel heterogeneous multi-wire indirect arc directed energy deposition for in-situ synthesis Al-Zn-Mg-Cu alloy: Process, microstructure and mechanical properties82A fatigue life posterior analysis approach for laser-directed energy deposition Ti-6Al-4V alloy based on pore-induced failures by kernel ridge18Long-short term memory networks for modeling track geometry in laser metal deposition6Deep learning object detection for optical monitoring of spatters in L-PBF32Advances and opportunities in high-throughput small-scale mechanical testing43Waiting time prediction for bottom-up vat photopolymerization11Bichromatic melt pool thermal measurement based on a Red, Green, and Blue camera: Application to additive manufacturing processes12Additive manufacturing: New paradigm for developing water splitting systems17A review of in-situ monitoring and process control system in metal-based laser additive manufacturing229FE2 Computations with Deep Neural Networks: Algorithmic Structure, Data Generation, and Implementation15Evaluation of classification models in limited data scenarios with application to additive manufacturing18NiTiHfX Shape Memory Alloy Actuation Strain Prediction1A numerical study on non-spherical droplet impact with solidification in additive manufacturing7Modeling of hierarchical solidification microstructures in metal additive manufacturing: Challenges and opportunities58A shape parameter approximation for the problem of thermal orientation and simulation in the additive manufacturing process0Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling40In-situ experimental and high-fidelity modeling tools to advance understanding of metal additive manufacturing86Machine learning-enabled predictions of as-built relative density and high-cycle fatigue life of Ti6Al4V alloy additively manufactured by laser powder bed fusion28A Bayesian defect-based physics-guided neural network model for probabilistic fatigue endurance limit evaluation44Process-structure multi-objective inverse optimisation for additive manufacturing of lattice structures using a physics-enhanced data-driven method39Fundamental requirements of a machine learning operations platform for industrial metal additive manufacturing23Ensemble learning based defect detection of laser sintering2Advantages of Hybrid Neural Network Architectures to Enhance Prediction of Tensile Properties in Laser Powder Bed Fusion5Uncertainty-aware fatigue-life prediction of additively manufactured Hastelloy X superalloy using a physics-informed probabilistic neural network48Benchmarking study between Lagamine and Comsol Solvers for Finite Element Thermal Analyses of Directed Energy Deposition Process0Review on laser directed energy deposited aluminum alloys134Data-driven approaches for computational modeling for plasticity, fatigue, and fracture behavior of alloys0Height consistency compensation in laser-directed energy deposition of thin-walled parts32Lazerinio įborinimo poveikis martensitinio sendinamo plieno paviršiaus sluoksnio struktūrai ir savybėms0Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning10Process-structure-property models for metal additive manufacturing using AI/ML approaches1Temperature in Additive Manufacturing: New Index for Optimization of Orientation0The Current Design for Additive Manufacturing Research Frontier19Unsupervised learning of part similarity for goal-guided accelerated experiment design in metal additive manufacturing2Optimizing surface roughness in soft pneumatic gripper fabricated via FDM: experimental investigation using Taguchi method15On the maintenance of processing stability and consistency in laser-directed energy deposition via machine learning27Energy Efficiency in Additive Manufacturing: Condensed Review49Error approximation and bias correction in dynamic problems using a recurrent neural network/finite element hybrid model7Railroad condition monitoring with distributed acoustic sensing: an investigation of deep learning methods for condition detection9Implementing machine learning in robotic wire arc additive manufacturing for minimizing surface roughness33Physics-Informed Machine Learning for metal additive manufacturing100Design of advanced steels by integrated computational materials engineering40An augmented surprise-guided sequential learning framework for predicting the melt pool geometry20Big data, machine learning, and digital twin assisted additive manufacturing: A review208Human-Centered and Sustainable Artificial Intelligence in Industry 5.0: Challenges and Perspectives250Machine Learning-Enhanced Process Parameter Optimization and Microstructure Prediction in Additive Manufacturing0A bi-fidelity DeepONet approach for modeling hysteretic systems under uncertainty1Finite element modeling and simulation of IN738 laser powder bed fusion process assisted via machine learning6Deep-learning based artificial intelligence tool for melt pools and defect segmentation10Machine learning enabling prediction in mechanical performance of Ti6Al4V fabricated by large-scale laser powder bed fusion via a stacking model15

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