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RESEARCH PAPER · Science China Technological Sciences · 2020

Machine learning of mechanical properties of steels

Jie Xiong, Tong‐Yi Zhang, San‐Qiang Shi

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Machine learning of mechanical properties of steelsOriginalInfluence of human activity on landslide susceptibility development in the Three Gorges area102Computational screening of the magnetocaloric materials1Development of robust surfaces for harsh service environments from the perspective of phase formation and transformation19Modelling IF Steels Using Artificial Neural Networks and Automated Machine Learning2Deep learning-assisted elastic isotropy identification for architected materials29Efficient parameters identification of a modified GTN model of ductile fracture using machine learning65Innovative Processes in Managing an Enterprise from the Energy and Food Sector in the Era of Industry 4.087Machine learning of phases and mechanical properties in complex concentrated alloys162Multi-task learning and data augmentation for negative thermal expansion materials property prediction12Study on strengthening effects of Zr-Ti-Nb-O alloys via high throughput powder metallurgy and data-driven machine learning29Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications128Machine learning for alloys698Explicit expressions of the saturation flux density and thermal stability in Fe-based metallic glasses based on Lasso regression17Data-driven phase recognition of steels for use in mechanical property prediction11A Prediction Method of Fracture Toughness of Nickel-Based Superalloys3Data-Driven Prediction of Probabilistic S-N Curves for Steels Based Oncomposition and Processing Parameters0Review on Data-driven Method for Property Prediction of Iron and Steel Wear-resistant Materials4SISSO-assisted prediction and design of mechanical properties of porous graphene with a uniform nanopore array25Using MARS for modeling and predicting tensile strength of low alloy steels2Using machine-learning to understand complex microstructural effects on the mechanical behavior of Ti-6Al-4V alloys36A data-driven machine learning approach to predict the hardenability curve of boron steels and assist alloy design27Accelerated Earth-Rockfill Dam Compaction by Collaborative Operation of Unmanned Roller Fleet29A weighted fuzzy C-means clustering method for hardness prediction6New insight in predicting martensite start temperature in steels15CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction37Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications995Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning91Establishing reduced-order process-structure linkages from phase field simulations of dendritic grain growth during solidification6Machine learning of the mechanical properties and data-driven design of lead-free solder alloys1Machine Learning-Based Modelling and Meta-Heuristic-Based Optimization of Specific Tool Wear and Surface Roughness in the Milling Process11A Strategic Approach to Machine Learning for Material Science: How to Tackle Real-World Challenges and Avoid Pitfalls71Accelerated Discovery of Ternary Gold Alloy Materials with Low Resistivity via an Interpretable Machine Learning Strategy7Ensemble Learning for Predicting the Tensile Strength of Alloy Steels from Chemical Composition and Processing Parameters1Predictions of Root Tensile Strength for Different Vegetation Species Using Individual and Ensemble Machine Learning Models9Machine Learning Based Sintered Density Prediction of Bronze Processed by Powder Metallurgy Route22Data driven discovery of an analytic formula for the life prediction of Lithium-ion batteries14Gaussian process regressions on hot deformation behaviors of FGH98 nickel-based powder superalloy48A computational method for the load spectra of large-scale structures with a data-driven learning algorithm12Fiber-Reinforced Polymer Confined Concrete: Data-Driven Predictions of Compressive Strength Utilizing Machine Learning Techniques23Ensemble models for predicting the hardness of alloy steels0A Cluster and Search Stacking Algorithm (CSSA) for predicting the ultimate bearing capacity of an HSS column2Understanding oxidation of Fe-Cr-Al alloys through explainable artificial intelligence14Discovering a formula for the high temperature oxidation behavior of FeCrAlCoNi based high entropy alloys by domain knowledge-guided machine learning41Predicting Synthesizability using Machine Learning on Databases of Existing Inorganic Materials34Application of Machine Learning to Regression Analysis of a Large SMA Weld Metal Database7Prediction and optimization of mechanical properties of Ni based and Fe–Ni based super alloys via neural network approach with alloying composition parameter15Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives241Property prediction for high-chromium high-vanadium steel based on transfer component analysis 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hyperparameter relationship for developing ANN for fragility prediction1Modeling Mechanical Properties of Industrial C-Mn Cast Steels Using Artificial Neural Networks11Review: machine learning approaches for diverse alloy systems46A novel multi-objective optimization intelligent technique applied to the design and manufacturing process of advanced transformation-induced plasticity aided martensitic steel0Machine Learning Approaches for Heat Treatment in Thermal Manufacturing Processes: A Review9Intelligent prediction of notch tensile strength in friction welded dissimilar stainless steel joints using machine learning2Differential Evolution-Enhanced Descriptor Selection for Low-Alloy Steel Performance Prediction0Analytical and Machine‑Learning Framework for Predicting the Impact of LPBF Parameters on the Hardness of Inconel™ 7180Directly Predicting Hot Ductility of Steels Using Machine Learning Approaches1Machine learning-driven predictive modeling of temperature-dependent mechanical properties in austenitic stainless steels1Machine-learning guides discovery of multi-principal element alloys as electrocatalyst for hydrogen evolution reaction3Machine Learning Applications in Predicting Friction Properties of Bearing Steel: A Review0Advances in interfacial engineering of MXene-based photocatalysts for solar CO 2 conversion3Algorithmic Diversity as Scientific Robustness: A Conceptual Framework0Application of machine learning algorithms for predicting mechanical properties of stainless steel0Comparative Analysis of Machine Learning Models for Engineering Material Classification Using Explainable Artificial Intelligence0From Explainable Predictions to Reliability-Aware Design: Interpretable Machine Learning for the Fatigue Strength of Steels0Machine learning-driven new paradigm for Co-based superalloys3Machine Learning-Driven Prediction of Microstructural Evolution and Mechanical Properties in Heat-Treated Steels Using Gradient Boosting3Data-Driven Prediction of Tensile Strength in Heat-Treated Steels Using Random Forests for Sustainable Materials Design3Influence of Alloying Elements and Annealing Temperature on Lankford Constant of High Strength Dual Phase Steels Using Supervised Regression Models0A comprehensive review of machine learning for superalloys: from data-driven prediction to intelligent design2Interpretable machine learning model for hot deformation behavior of Ti-Al-V-Fe-Cr armor titanium alloy0Machine learning for modeling and visualizing structure-property relationships in in-situ aluminum matrix composites1Machine Learning Framework for Predicting Mechanical Properties of Heat-Treated Alloys: Computational Approach2Novel Analytical and Machine Learning Framework Predicting LPBF Heat Treatment Effects on Inconel™ 718 Hardness0Machine Learning–Based Prediction of the Constitutive Model of Austenitic Stainless Steels at Room and Elevated Temperatures0

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