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RESEARCH PAPER · GAMM-Mitteilungen · 2021

Machine learning for material characterization with an application for predicting mechanical properties

Anke Stoll, Peter Benner

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Machine learning for material characterization with an application for predicting mechanical propertiesOriginalHybrid Machine Learning Techniques and Computational Mechanics: Estimating the Dynamic Behavior of Oxide Precipitation Hardened Steel29Experimental study on creep properties prediction of reed bales based on SVR and MLP9Application of Machine Learning in Determining the Mechanical Properties of Materials22Machine Learning: Supervised Algorithms to Determine the Defect in High‐Precision Foundry Operation110Compatibility and challenges in machine learning approach for structural crack assessment21Machine Learning–Assisted Design of Material Properties80Assessment of the Quality and Mechanical Parameters of Castings Using Machine Learning Methods14Data-Driven Derivation of Sheet Metal Properties Gained from Punching Forces Using an Artificial Neural Network17Machine-Learning-Based Predictions of Polymer and Postconsumer Recycled Polymer Properties: A Comprehensive Review62Learning the stress-strain fields in digital composites using Fourier neural operator77Quantifying alignment deviations for uniaxial material mechanical testing via automated machine learning10Predicting Misalignments of the Material Test System through a Thick-flat Specimen by Machine Learning0A Conceptual Distinction between Generalization and Transfer in Materials Machine Learning0Machine Learning in Computer Aided Engineering12Machine learning in materials synthesis and characterization0Self-Supervised Representation Learning of Microstructures from Electron Microscopy for Property Prediction0Tackling data scarcity with transfer learning: a case study of thickness characterization from optical spectra of perovskite thin films14Perspectives on development of biomedical polymer materials in artificial intelligence age16A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method17Estimation of fatigue life of welded structures incorporating importance analysis of influence factors: A data-driven approach34An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials96Prediction of mechanical properties of composite materials using multimodal fusion learning64Reinforced active learning for CVD-grown two-dimensional materials characterization3Screening outstanding mechanical properties and low lattice thermal conductivity using global attention graph neural network24Insight into the anti-corrosive performance of Persea Americana seed extract as a high-efficiency and sustainable corrosion inhibitor for API 5 L X65 pipeline steel in 1 M HCl solution10Predicting elastic and plastic properties of small iron polycrystals by machine learning10Prediction of mechanical properties for typical pressure vessel steels by small punch test combined with machine learning27Modeling Yield Strength of Austenitic Stainless Steel Welds Using Multiple Regression Analysis and Machine Learning3Optimizing interlaminar toughening of carbon-based filler/polymer nanocomposites by machine learning10Mechanical characterization of human skin—A non-invasive digital twin approach using vibration-response integrated with numerical methods9Evaluation of defect-related fatigue performance of additive manufacturing GH4169 via small punch test22The Extraction of Copper from Chalcopyrite Concentrate with Hydrogen Peroxide in Sulfuric Acid Solution23Scope of machine learning in materials research—A review230Application and Challenges of Machine Learning Techniques in Mining Engineering and Material Science4Applications of Deep Learning for Composites Materials2Artificial Intelligence and Machine Learning for Material Characterizations and Mechanical Properties7Neural Nonparametric Stability Indicator for Self-Excited Dynamical Systems0RETRACTED: Comparative Review on Machine Learning-Based Predictive Modeling for Mechanical Characterization2RETRACTED: Review of Multiscale Modeling and Simulation Techniques in Metal Forming, Bending, Welding, and Casting Processes for Enhanced Predictive Design and Analysis5The Short Video Popularity Prediction Using Internet of Things and Deep Learning7Towards data-efficient mechanical design of bicontinuous composites using generative AI21Application of Machine Learning Tools to Material Science: A Mini-Review5Exploring deep learning and machine learning for novel red phosphor materials11Prediction of local elasto-plastic stress and strain fields in a two-phase composite microstructure using a deep convolutional neural network63A correlation study on piezo-embedded carbon fibre reinforced polylactic acid composite: Experimental and numerical modelling6A Comparative Study on Various ANN Optimization Algorithms for Magnetorheological Elastomer Carbonyl Iron Particle Concentration Estimation1Predictive Modeling of Thin Film Yield Stress Using Machine Learning: A Simulation-Based Approach5Hyperspectral vision beyond 3D: A review33A Method for Straightening Distorted Giga-Cast Large Thin-Walled Components5Artificial Neural Network-Based Modelling for Yield Strength Prediction of Austenitic Stainless-Steel Welds7A Data-Driven Machine Learning Model for the Stress-Strain Behavior of Single Grain SAC305 Solder Joints2Prediction of the Mechanical Responses of Single Grain Lead-Free Solder Joints Using Machine Learning1Predictive analytics of wear performance in high entropy alloy coatings through machine learning31An efficient shape-based procedure for strain hardening identification in the post-necking phase5Deformation stage identification in steel material using acoustic emission with a hybrid denoising method and artificial neural network19Taylor‐based smart flower optimization algorithm with the deep residual network to predict mechanical materials properties1Application of artificial intelligence in the materials science, with a special focus on fuel cells and electrolyzers78Prediction of Acrylonitrile–Butadiene–Styrene Mechanical Properties through Compressed-Sensing Techniques1Integrating Phase Field Modeling and Machine Learning to Develop Process-Microstructure Relationships in Laser Powder Bed Fusion of IN71815Reverse characterization prediction of diamond Schottky barrier power devices using machine learning: Predicting breakdown voltage and Baliga figure of merit5Understanding the role of impurities in the precipitation kinetics in Al-Sc-Zr alloys0Enhancing tensile strength, adhesive joining of CFPEEK and microstructure properties of epoxy by Nd2O3 rare-earth nanoparticles reinforcement20Review of Image Processing Methods for Surface and Tool Condition Assessments in Machining36Atomic fingerprints for high throughput screening of 2D monolayers2Machine learning–guided optimization of coercive field in Al 1− x Sc x N thin films for nonvolatile memory0Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through image enhancement9DetectNano: deep learning detection in TEM images for high-throughput nanostructure characterization10Machine Learning Algorithms for Applications in Materials Science II0Mechanical properties estimation of multi-layer friction stir plug welded aluminium plates using time-series neural network models2Optimising geometry of weld beads for high-performance welding of hot rolled carbon steel by taguchi technique0Study on stress prediction model of EH36 steel in polar environments: Optimization of basis functions using adaptive genetic algorithm and simulated annealing4Machine learning approaches to natural fiber composites: A review of methodologies and applications20Advancements of machine learning techniques in fiber-filled polymer composites: a review18On the Sufficiency of a Single Hidden Layer in Feed-Forward Neural Networks Used for Machine Learning of Materials Properties5Novel texture analysis method for optimising material property in extruded 6xxx alloys using artificial neural networks4A machine learning approach to predicting the spall strength of metals and alloys4A Surface-Enabled Computational Homogenization Method for Variable-Density Polymer Lattice Metastructures2Comprehensive Toughness Dataset of Nuclear Reactor Structural Materials using Charpy V-Notch Impact Testing2Hybrid Data Driven Deep Learning Framework for Material Property Prediction1Integrating Deep Learning Into Mechanical Engineering: A Systematic Review of Applications and Educational Implications9Predictive modeling of corrosion rate of horizontal wellbore in Qingcheng shale oil0Predicting ultimate tensile and break strength of SLS PA 12 parts using machine learning on tensile load–displacement data5Transforming Computational Nanotechnology: Accelerating Material Discovery, Design, and Property Prediction through Soft Computing Techniques24Machine learning prediction of mechanical properties of metallic materials: data augmentation strategies1Artificial Intelligence for Materials Discovery, Development, and Optimization171Physics-informed deep learning approach for nanoindentation-based thin film analysis4Theory-guided machine learning for strength prediction of polymers at different temperatures and strain rates with physical information2Fine-Tuned Large Language Models for High-Accuracy Prediction of Band Gap and Stability in Transition Metal Sulfides2Next-Generation Biopolymers for Sustainable Food Packaging: Innovations in Material Science, Circular Economy, and Smart Technologies19A Comprehensive Study on Fatigue Strength Prediction of a Broad Range of Steels Using Machine Learning2Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review50Analytical and Machine‑Learning Framework for Predicting the Impact of LPBF Parameters on the Hardness of Inconel™ 7180Machine Learning Applications in Sustainable Construction Materials: A Scientometrics Review of Global Trends, Themes, and Future Directions11Data-Driven Optimization of Discontinuous and Continuous Fiber Composite Processes Using Machine Learning: A Review36Machine learning guided process optimization and sustainable valorization of coconut biochar filled PLA biocomposites6A new era in energy storage: In Situ and Operando characterization of 2D materials-based devices64Review of machine learning approaches for predicting mechanical behavior of composite materials83Machine learning methods for cold-formed stainless steel roof panel temperature prediction0Alloy Quality Assessment using Meta-Ensemble Classifier by BERT-based Transfer Learning0Explanatory Ensemble Model for the Material Selection Process using GB Algorithms0Real-Time Surface Defect Detection and Hardness Prediction in Metals Using YOLOv80Machine Learning Applied to High Entropy Alloys under Irradiation8Rubber composite for nano-generators and use of artificial intelligence as smart electronic systems: A critical review4Mol2Raman: a graph neural network model for predicting Raman spectra from SMILES representations4Datasets and tools in materials science0Development of a Machine Learning-Based Predictive Model for the Structural Safety of an Optical Table Air Springs Using Simulation Data1A Hybrid Conditional Diffusion-DeepONet Framework for High-Fidelity Stress Prediction in Hyperelastic Materials0AI in material characterization0Analysis of Enhanced Predictive Models on Mechanical Behavior in Natural Fiber Composites Using Linear Regression Algorithms1Generative diffusion modeling protocols for improving the Kikuchi pattern indexing in electron back-scatter diffraction0Neural networks in predicting mechanical properties of Al-Zn alloys: A review and model development0Non-destructive evaluation of material hardness using micromagnetic sensors and complex neural networks0Effect of Fe-Ni-Al content on mechanical responses and deformation mechanisms of Fe-Ni-Cr-Co-Al high-entropy alloys: insights from molecular dynamics simulation and machine learning2Structural materials in Frontiers in Materials, 10 years on0Material Classification Using Decision Tree Algorithm, Support Vector Machine, and K-Means Clustering based on Density and Strength1Graph representation learning for predicting processing conditions in Spinodal decomposition microstructures1High-Precision Classification of Transparent Conductive Oxides with Optical Signatures using XGBoost Algorithm for Accelerated Material Screening2Machine Learning-Enhanced Molecular Dynamics: Current State, Challenges and Perspectives5Mat-GPT Estimation of Material Properties0Prediction of mechanical properties of magnesium alloys through machine learning: a review0Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning0Machine learning for analysis and fourier transform infrared spectroscopy characterization of titanium hydroxyapatite composite1A Fatigue Model Incorporating Temperature Effect for Predicting High-Cycle Fatigue Life of Alloys0A strength prediction hybrid framework of fiber reinforced composites by integrating physics-informed deep learning and generative AI2Novel Analytical and Machine Learning Framework Predicting LPBF Heat Treatment Effects on Inconel™ 718 Hardness0Hybrid experimental-machine learning evaluation of fractal geometrical effects on rammed earth materials strength0A Literature Review on Machine Learning Applications in Mechanical Engineering0Optimizing acoustic emission features for pipeline crack detection using neural networks0Deep Learning Network‐Tailored Microenvironment Matching of 4D Bioprinting Bioactive Scaffolds for Bone Regeneration0Computational analysis of polymer carbon nanotube composites using experiments and ANN modeling for manufacturing applications0Evaluation of ensemble machine-learning models for mechanical-property prediction in titanium alloys with SHAP-based interpretability0An ML-driven web interface for classification and optical analysis of transparent conductive oxide materials0Generative diffusion modeling protocols for improving the Kikuchi pattern indexing in electron backscatter diffraction0Improving Adhesive Bondline Time of Flight Predictions During Autoclave Cure Utilizing Machine Learning0Quantifying uncertainty in void swelling prediction: A conformal prediction framework for reactor safety margins0Recent progress in machine learning strategies in designing, synthesizing and characterizing new age metal matrix composites0Machine Learning-Driven Lightweight Design of Materials for Police Protective Equipment: A Methodological Framework and Validation Study0Explainable machine learning approach for predicting mechanical characteristics of AA6082–AA7075 friction stir welded joints0

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  2. Experimental study on creep properties prediction of reed bales based on SVR and MLP

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  8. Data-Driven Derivation of Sheet Metal Properties Gained from Punching Forces Using an Artificial Neural Network

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  11. Quantifying alignment deviations for uniaxial material mechanical testing via automated machine learning

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  48. Hyperspectral vision beyond 3D: A review

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  50. Artificial Neural Network-Based Modelling for Yield Strength Prediction of Austenitic Stainless-Steel Welds

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