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RESEARCH PAPER · Scripta Materialia · 2020

A strategy combining machine learning and multiscale calculation to predict tensile strength for pearlitic steel wires with industrial data

Xue Feng Jiang, Baorui Jia, Guofei Zhang, Cong Zhang, Xin Wang, Ruijie Zhang, Haiqing Yin, Xuanhui Qu, Yong Hui Song, Lan Su, Zhenli Mi, Lei Hu, Han Ma

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42 direct citing works

A strategy combining machine learning and multiscale calculation to predict tensile strength for pearlitic steel wires with industrial dataOriginalRelationship between mesoscale structure and ductility of drawn high carbon steel wire18A screening strategy for hot forging combining high-throughput forging experiment and machine learning3Uniformity and comprehensive quality evaluation of steel product via process capability index and TOPSIS6In-situ prediction of α-phase volume fraction in titanium alloy using laser ultrasonic with support vector regression23Modeling of Alloying Effect on Isothermal Transformation: A Case Study for Pearlitic Steel18A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys35Analysis of Critical Velocity of Cold Spray Based on Machine Learning Method with Feature Selection46Machine Learning‐Guided Design of Pearlitic Steel with Promising Mechanical and Tribological Properties9Machine Learning‐Aided Process Design: Modeling and Prediction of Transformation Temperature for Pearlitic Steel26Deep Convolutional Neural Network Modeling and Laplace Transformation Algorithm for the Analysis of Surface Quality of Friction Stir Welded Joints5Machine Learning Applications for The Tensile Property Evaluation of Steel: An Overview0Machine Learning Applications for The Tensile Property Evaluation of Steel: An Overview0High strength aluminum alloys design via explainable artificial intelligence51Automated pipeline for superalloy data by text mining83Text Mining for Energy Materials3Physical Metallurgy Guided Industrial Big Data Analysis System with Data Classification and Property Prediction18A data-driven machine learning approach to predict the hardenability curve of boron steels and assist alloy design27Prediction of Oxygen Evolution Activity for NiCoFe Oxide Catalysts via Machine Learning31Metallurgical Data Science for Steel Industry: A Case Study on Basic Oxygen Furnace32Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning91Using Machine Learning to Predict Oxygen Evolution Activity for Transition Metal Hydroxide Electrocatalysts39Physical metallurgy guided deep learning for yield strength of hot-rolled steel based on the small labeled dataset27A strategy combining machine learning and physical metallurgical principles to predict mechanical properties for hot rolled Ti micro-alloyed steels62Data-driven pitting evolution prediction for corrosion-resistant alloys by time-series analysis23Accelerated alloy discovery using synthetic data generation and data mining20Predicting failure progressions of structural materials via deep learning based on void topology19Advances in machine learning- and artificial intelligence-assisted material design of steels109Prediction of the tensile properties of ultrafine grained Al–SiC nanocomposites using machine learning69Effect of Cr and Cooling Rates in Hypoeutectoid Steels Under Forced-Convection Conditions0Improved material descriptors for bulk modulus in intermetallic compounds via machine learning27Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel16Deep learning-based method for microstructure-property linkage of dual-phase steel22Multiobjective Ensemble Learning With Multiscale Data for Product Quality Prediction in Iron and Steel Industry43Semi-Solid Deformation Behavior, Flow Stress Prediction and Deformed Microstructure of GH3536 Superalloy1Tensile Strength Prediction of Gray Cast Iron for Cylinder Head Based on Microstructure and Machine Learning4Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology78Tempered Hardness Optimization of Martensitic Alloy Steels6A quantitative relation for the ductile-brittle transition temperature in pipeline steel27Evolutionary modeling approach based on multiobjective genetic programming for strip quality prediction6Machine learning-based performance predictions for steels considering manufacturing process parameters: a review26Exploring the Ambient-Temperature Degradation Reactions of PET through Two-Step Machine Learning and High-Throughput Experimentation11Prediction improvement of compressive strength and strain of directionally solidified TiAl alloy based on training data size adjustment4Prediction of spring steel wire rod hardness based on wire rod rolling process data: a case study2A materials terminology knowledge graph automatically constructed from text corpus21Construction of a prediction model for properties of wear-resistant steel using industrial data based on machine learning approach6Machine Learning Approach to Investigate High Temperature Corrosion of Critical Infrastructure Materials8Optimization of Eutectic Carbide Inhomogeneity in M42 High‐Speed Steel through Machine Learning and Finite‐Element Modeling1A knowledge graph attention network for the cold‐start problem in intelligent manufacturing: Interpretability and accuracy improvement6Industrial big data analysis strategy based on automatic data classification and interpretable knowledge graph4Applications of natural language processing and large language models in materials discovery132Interpretable Machine Learning Applications: A Promising Prospect of AI for Materials68Data-driven electrochemical behavior prediction for refractory high-entropy alloys by global and focused learning5DRBoost: A Learning-Based Method for Steel Quality Prediction1Machine learning for predicting high-temperature strength of austenitic stainless steels based on composition and deformation parameters4AI-powered language models for alloy design and laser-based manufacturing: A review of NLP applications in materials science16Mechanical property prediction of high-speed wire based on multi-modal deep network with multi-task learning1A Semi‐Supervised Transfer Approach Based on Pairwise Learning for Tensile Strength Prediction of Hot‐Rolled Cord Steel0Prediction of yield strength of hot-rolled rebar and process design under guidance of physical metallurgy0Machine Learning for Structural Steels: Materials Design, Property Prediction, Durability, and Future Directions0Modeling and control of Stelmor cooling process: advances, challenges and perspectives0

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  2. A screening strategy for hot forging combining high-throughput forging experiment and machine learning

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  3. Uniformity and comprehensive quality evaluation of steel product via process capability index and TOPSIS

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  4. In-situ prediction of α-phase volume fraction in titanium alloy using laser ultrasonic with support vector regression

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  5. Modeling of Alloying Effect on Isothermal Transformation: A Case Study for Pearlitic Steel

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  6. A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys

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  7. Analysis of Critical Velocity of Cold Spray Based on Machine Learning Method with Feature Selection

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  8. Machine Learning‐Guided Design of Pearlitic Steel with Promising Mechanical and Tribological Properties

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  9. Machine Learning‐Aided Process Design: Modeling and Prediction of Transformation Temperature for Pearlitic Steel

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  10. Deep Convolutional Neural Network Modeling and Laplace Transformation Algorithm for the Analysis of Surface Quality of Friction Stir Welded Joints

    Gredos (University of Salamanca) · 2021-10-01

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  11. Machine Learning Applications for The Tensile Property Evaluation of Steel: An Overview

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  13. High strength aluminum alloys design via explainable artificial intelligence

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  17. A data-driven machine learning approach to predict the hardenability curve of boron steels and assist alloy design

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  18. Prediction of Oxygen Evolution Activity for NiCoFe Oxide Catalysts via Machine Learning

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  19. Metallurgical Data Science for Steel Industry: A Case Study on Basic Oxygen Furnace

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  20. Identifying facile material descriptors for Charpy impact toughness in low-alloy steel via machine learning

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  21. Using Machine Learning to Predict Oxygen Evolution Activity for Transition Metal Hydroxide Electrocatalysts

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  22. Physical metallurgy guided deep learning for yield strength of hot-rolled steel based on the small labeled dataset

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  23. A strategy combining machine learning and physical metallurgical principles to predict mechanical properties for hot rolled Ti micro-alloyed steels

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  24. Data-driven pitting evolution prediction for corrosion-resistant alloys by time-series analysis

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  25. Accelerated alloy discovery using synthetic data generation and data mining

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