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RESEARCH PAPER · Computational Materials Science · 2019

A predicting model for properties of steel using the industrial big data based on machine learning

Shun Guo, Jinxin Yu, Xingjun Liu, Cuiping Wang, Qingshan Jiang

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A predicting model for properties of steel using the industrial big data based on machine learningOriginalDevelopment and Application of Performance Prediction Model for Cold-Rolled Products0Development of a prediction method of Rayleigh damping coefficients for free layer damping coatings through machine learning algorithms40Tensile property prediction by feature engineering guided machine learning in reduced activation ferritic/martensitic steels75Agro Waste A Sustainable Source For Steel Reinforcement-Review23A steel property optimization model based on the XGBoost algorithm and improved PSO201Elevating Prediction Performance for Mechanical Properties of Hot-Rolled Strips by Using Semi-Supervised Regression and Deep Learning21Machine Learning and Big Data Provide Crucial Insight for Future Biomaterials Discovery and Research104Machine Learning as a Smart Manufacturing Tool1Prediction for Manufacturing Factors in a Steel Plate Rolling Smart Factory Using Data Clustering-Based Machine Learning45Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks41Composition-Processing-Property Correlation Mining of Nb–Ti Microalloyed Steel Based on Industrial Data10Parametric Investigation of Particle Swarm Optimization to Improve the Performance of the Adaptive Neuro-Fuzzy Inference System in Determining the Buckling Capacity of Circular Opening Steel Beams29Intelligent state of health estimation for lithium-ion battery pack based on big data analysis195Structured Methodology for Clustering Gas Turbine Transients by Means of Multi-Variate Time Series3Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach62Machine learning-guided design and development of metallic structural materials27Reducing Crop Length of Rolling Material Using Predictive Model Analysis0Structured Methodology for Clustering Gas Turbine Transients by Means of Multivariate Time Series7Optimization of Dephosphorization Parameter in Consteel Electric Arc Furnace Using Rule Set Model6Prediction of Mechanical Properties of Wrought Aluminium Alloys Using Feature Engineering Assisted Machine Learning Approach62Prediction of the Charpy V-notch impact energy of low carbon steel using a shallow neural network and deep learning46The Steelmaking Process Parameter Optimization with a Surrogate Model Based on Convolutional Neural Networks and the Firefly Algorithm6Experimental Research on the Performance of High-Temperature Bearing Steel Cr4Mo4 V0Accelerated discovery of high-performance Cu-Ni-Co-Si alloys through machine learning84Machine Learning Model for Thickness Evolution of Oxide Scale During Hot Strip Rolling of Steels29Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions51Development of an Integrated Flow Stress and Roll Force Models for Plate Rolling of Microalloyed Steel10A model for predicting silica concentrate concertration based on Bayesian-LGC0Analysis of Soft Computing Techniques to Forecast Mechanical Attributes of an Alloy0Big Data in Forecasting Research: A Literature Review61Explainable artificial intelligence: a comprehensive review969Machine Learning Applications for The Tensile Property Evaluation of Steel: An Overview0Method for Predicting Compound Properties in Drug Development Based on Machine Learning1Explainable Steel Quality Prediction System Based on Gradient Boosting Decision Trees31Machine Learning Applications for The Tensile Property Evaluation of Steel: An Overview0Mechanical properties prediction of dual phase steels using machine learning8Microstructure Generating Algorithm for Martensitic Alloys: Application to High-Strength Steels0Review on Data-driven Method for Property Prediction of Iron and Steel Wear-resistant Materials4A comparison of machine learning- and regression-based models for predicting ductility ratio of RC beam-column joints46Imputation Method Based on Collaborative Filtering and Clustering for the Missing Data of the Squeeze Casting Process Parameters7Endpoint Phosphorus Content Prediction in BOF Steelmaking Using PCA-Based Support Vector Regression1Using machine-learning to understand complex microstructural effects on the mechanical behavior of Ti-6Al-4V alloys36Physical Metallurgy Guided Industrial Big Data Analysis System with Data Classification and Property Prediction18Digital Twin-Enabled Production Optimization for Steel Industry5Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications995Data-Driven Construction Method of Material Mechanical Behavior Model3On necessity of taking into account statistical nature of the objects using Big Data in metallurgy6Prediction Model for Vanadium Content in Vanadium and Titanium Blast Furnace Smelting Iron Based on Big Data Mining13Artificial Intelligence, Machine Learning, and Deep Learning in Structural Engineering: A Scientometrics Review of Trends and Best Practices317Deep Neural Network Prediction Model of Hydrogen Content in VOD Process Based on Small Sample Dataset3Classification of Nonmetallic Inclusions in Steel by Data‐Driven Machine Learning Methods11Physical metallurgy guided deep learning for yield strength of hot-rolled steel based on the small labeled dataset27Design of new Al-Si-Mg alloys by multi-modal mixed input simulation experiment7A strategy combining machine learning and physical metallurgical principles to predict mechanical properties for hot rolled Ti micro-alloyed steels62Benchmarking AutoML for regression tasks on small tabular data in materials design48Process parameters design of squeeze casting through an improved KNN algorithm and existing data24Ensemble Learning for Predicting the Tensile Strength of Alloy Steels from Chemical Composition and Processing Parameters1Dimensionality reduction for machine learning using statistical methods: A case study on predicting mechanical properties of steels14Excellent Balance of Ultimate Tensile Strength and Ductility in a Ti–6Al–2Sn–4Zr–2Mo–Si Alloy Having Duplex α + α′ Microstructure: Effect of Microstructural Factors from Experimental Study and Machine Learning10Explainable Intrusion Detection for Cyber Defences in the Internet of Things: Opportunities and Solutions263Impact of Data Sampling on Performance and Robustness of Machine Learning Models in Production Engineering4The effect of element characteristics on bainite transformation start temperature using a machine learning approach13Automatic Identification of the Multiphase Microstructures of Steels based on ASPP‐FCN4Need for UAI–Anatomy of the Paradigm of Usable Artificial Intelligence for Domain-Specific AI Applicability19Machine learning study on time–temperature–transformation diagram of carbon and low-alloy steel10Material Stiffness Prediction Based on Neural Network and Symbolic Regression0Artificial-intelligence-led revolution of construction materials: From molecules to Industry 4.0103Estimation for Microstructure Formation Factor in TRIP-aided Multi-phase Steel by Using Primary Component Analysis of Data on the Elemental Distribution1Prediction of Mechanical Properties of Aluminium Alloy Strip Using the Extreme Learning Machine Model Optimized by the Gray Wolf Algorithm5Data-driven machine learning for alloy research: Recent applications and prospects21Composition design of high-performance copper alloy by coupling artificial neural network and genetic algorithm10Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology78Modeling Yield Strength of Austenitic Stainless Steel Welds Using Multiple Regression Analysis and Machine Learning3An Effective Framework for Predicting Performance of Solid-Solution Copper Alloys Using a Feature Engineering Technique in Machine Learning3Application of machine learning methods for the prediction of roll force and torque during plate rolling of micro-alloyed steel22A Systematic Review of Medium‐Mn Steels with an Assessment of Fatigue Behavior20Data driven performance prediction of titanium-based matrix composites5Prediction of the Fatigue Strength of Steel Based on Interpretable Machine Learning25Artificial Intelligence and Machine Learning In Metallurgy. Part 2. Application Examples9A dual-population multiobjective co-evolutionary matching ensemble learning for product multi-indicator prediction in continuous annealing6Evolutionary modeling approach based on multiobjective genetic programming for strip quality prediction6Design of high performance Cu-Ni-Si alloys via a multiobjective strategy based on machine learning12Multiobjective Evolutionary Learning for Multitask Quality Prediction Problems in Continuous Annealing Process10Artificial Neural Network-Based Modelling for Yield Strength Prediction of Austenitic Stainless-Steel Welds7Symbolic regression as a feature engineering method for machine and deep learning regression tasks19Machine Learning Combining High-Temperature Experiments for the Prediction of Wetting Angle of Mold Fluxes3Prediction and analysis of mechanical properties of hot-rolled strip steel based on an interpretable machine learning21Development of a model for iron concentrate tonnage with least angle regressions – An industrial trial0Sealing rubber ring design based on machine learning algorithm combined progressive optimization method18Application of artificial intelligence to enhance manufacturing quality and zero-defect using CRISP-DM framework10Empowering the Sustainable Development of High‐End Alloys via Interpretive Machine Learning52Prediction model of mechanical properties of hot-rolled strip based on improved feature selection method5Application of Combined Machine Learning Methods in Metallurgy FOR Analysis, Selection of Materials and Prediction of Properties2Excellent Balance of Ultimate Tensile Strength and Ductility in a Ti-6Al-2Sn-4Zr-2Mo-Si Alloy Having Duplex α+α′ Microstructure: Effect of Microstructural Factors from Experimental Study and Machine Learning2Machine and deep learning performance in out-of-distribution regressions6Chemical feature-based machine learning model for predicting photophysical properties of BODIPY compounds: density functional theory and quantitative structure–property relationship modeling3A hybrid knowledge-guided and data-driven method for predicting low-alloy steels performance7Machine learning accelerate the design of low cost and high strength Cu-Zn alloys based on the small sample dataset0Machine learning accelerate the design of low cost and high strength Cu-Zn alloys based on the small sample dataset0Quantifying the effects of cooling rates on Fe-rich intermetallics in recycled Al-Si-Cu alloys by machine learning6Leveraging multi-task learning regressor chains for small and sparse tabular data in materials design8Design of high strength and ductile recycled Al alloys by machine learning7Machine learning-driven predictive modeling of mechanical properties in diverse steels8Prediction of Creep Rupture Life of 5Cr-0.5Mo Steel Using Machine Learning Models9Industrial big data analysis strategy based on automatic data classification and interpretable knowledge graph4Prediction of mechanical property and costs of high-strength steel using machine learning3Comparison of semi-empirical models, symbolic regression, and machine learning approaches for prediction of tensile strength in steels2Machine learning assisted design of reactor steels with high long-term strength and toughness4Using machine learning as a supportive tool to systematically study the tensile properties of a Ti-6Al-2Sn-4Zr-2Mo-Si alloy with various microstructures: Effects of texture, phase proportions and grain size6Progress in Modelling Microstructural Evolution and Changes of Mechanical Properties for Hot Rolled Steels -The Path from Semi-empirical through Machine Learning to Industrial Foundation Models4Machine learning surrogates for predicting axial strength in concrete-filled stainless-steel tubular columns10Data-driven prediction of ultimate tensile strength in low alloy steel: A machine learning approach8Iron-Based High-Temperature Alloys: Alloying Strategies and New Opportunities19Possible consequences caused by reducing the number of samples during acceptance tests of a unit of metal products to assess its quality1Advancing chemical engineering technology with artificial intelligence21Machine Learning-Based Prediction of Mechanical Properties for Large Bearing Housing Castings5Prediction of mechanical properties of 16Mn large tube plates based on machine learning0The Controlling of Longitudinal Cracks Defect Based on BO-XGBoost Model4DRBoost: A Learning-Based Method for Steel Quality Prediction1Machine Learning Approaches for Fatigue Life Prediction of Steel and Feature Importance Analyses12Machine learning-driven predictive modeling of temperature-dependent mechanical properties in austenitic stainless steels1High-temperature activity coefficient prediction: Thermodynamic-Machine learning synergistic modeling of binary Mg-based alloy system at 500 °C4Interpretable Prediction of Mechanical Properties for Hot‐Rolled Seamless Steel Pipes: Combining Machine Learning with Shapley Additive Explanation0Physics-informed boosting-based machine learning prediction of martensitic start temperature and thermal hysteresis in Cu–Al–Ni shape memory alloy1Machine learning accelerate the design of low cost and high strength CuZn alloys based on the small sample dataset1Advanced Materials, Machinability and Intelligent Manufacturing Systems0An Efficient Multi-objective Evolutionary Learning Framework for Multi-Indicator Interval Prediction of Steel Properties in Continuous Annealing Process0Overview of machine learning in civil engineering0Simulation-augmented learning for ballistic performance prediction of ultra-high-strength steel0Application of Intelligent Systems in CO 2 Management: State of the Art and Future Prospects0An interpretable layered stacking framework for multi-target mechanical property prediction of TMCP X80 pipeline steel0Machine Learning for Structural Steels: Materials Design, Property Prediction, Durability, and Future Directions0A federated learning–driven data fusion strategy for the hardenability prediction of gear steel0Interpretable prediction of steel mechanical properties via physics-informed constraints and Kolmogorov–Arnold networks0Decoding high-temperature activity coefficients in magnesium alloys through interpretable data-driven thermodynamics0Machine Learning-Based Prediction of Ultimate Tensile Strength in 9% Cr Steels Using Composition and Temperature0

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