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Phase Prediction of High Carbon Pearlitic Steel: An Improved Model Combining Mind Evolutionary Algorithm and Neural Networks

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Modeling of Alloying Effect on Isothermal Transformation: A Case Study for Pearlitic Steel
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Multi‐Layer Feature Selection Incorporating Weighted Score‐Based Expert Knowledge toward Modeling Materials with Targeted Properties
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Mechanical Performance‐Based Optimum Design of High Carbon Pearlitic Steel by Particle Swarm Optimization
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Visualization Method of Trajectory Data Based on GML, KML
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Microstructure–Property Relationship in Cold-Drawn Pearlitic Steel Wires
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A Mathematical Model to Predict Microstructure of Heat-Treated Steel
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Application of generalized regression neural network optimized by fruit fly optimization algorithm for fracture toughness in a pearlitic steel
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Machine learning assisted materials design and discovery for rechargeable batteries
inferredcites →https://doi.org/10.1016/j.jallcom.2011.10.102openalex-snapshot
Microtwinning as a common mechanism for the martensitic and pearlitic transformations
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The influence of chromium on the pearlite-austenite transformation kinetics of the Fe–Cr–C ternary steels
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Effects of Ni and Cu addition on cryogenic-temperature tensile and Charpy impact properties in austenitic 22Mn-0.45C–1Al steels
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Effect of Al on phase evolution in Prior-Ni coated galvanized steel: Thermodynamic and kinetic assessment
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Modelling and prediction of hardness in multi-component alloys: A combined machine learning, first principles and experimental study
inferredcites →https://doi.org/10.1016/j.jmat.2017.08.002openalex-snapshot
Materials discovery and design using machine learning
inferredcites →https://doi.org/10.1016/j.matdes.2011.08.049openalex-snapshot
Application of artificial neural network and constitutive equations to describe the hot compressive behavior of 28CrMnMoV steel
inferredcites →https://doi.org/10.1016/j.msea.2008.06.009openalex-snapshot
Microstructure based modeling for the mechanical behavior of ferrite–pearlite steels suitable to capture isotropic and kinematic hardening
inferredcites →https://doi.org/10.1016/j.msea.2012.10.012openalex-snapshot
Generalization of the existing relations between microstructure and yield stress from ferrite–pearlite to high strength steels
inferredcites →https://doi.org/10.1016/j.msea.2017.12.074openalex-snapshot
Effect of Sn content on strain hardening behavior of as-extruded Mg-Sn alloys
inferredcites →https://doi.org/10.1016/j.msea.2018.07.099openalex-snapshot
Effect of nanostructuring frictional treatment on the properties of high-carbon pearlitic steel. Part I: microstructure and surface properties
inferredcites →https://doi.org/10.1016/j.msea.2018.09.067openalex-snapshot
Evaluation of local strength via microstructural quantification in a pearlitic rail steel deformed by simultaneous compression and torsion
inferredcites →https://doi.org/10.1016/j.msea.2020.139845openalex-snapshot
Application of improved GRNN model to predict interlamellar spacing and mechanical properties of hypereutectoid steel
inferredcites →https://doi.org/10.1016/j.mtcomm.2020.101134openalex-snapshot
Modelling and prediction of thermal deformation behaviors in a pearlitic steel
inferredcites →https://doi.org/10.1016/j.ymssp.2017.09.039openalex-snapshot
Distributed collaborative probabilistic design of multi-failure structure with fluid-structure interaction using fuzzy neural network of regression
inferredcites →https://doi.org/10.1016/s0924-0136(02)00278-9openalex-snapshot
Modelling of microstructure and mechanical properties of steel using the artificial neural network
inferredcites →https://doi.org/10.1038/s41598-019-43623-7openalex-snapshot
Improvement of wear resistance in a pearlitic rail steel via quenching and partitioning processing
inferredcites →https://doi.org/10.1039/c9ta06748eopenalex-snapshot
Rationalizing the interphase stability of Li|doped-Li 7 La 3 Zr 2 O 12 via automated reaction screening and machine learning
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Multi-scale computation methods: Their applications in lithium-ion battery research and development
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Short term load forecasting using fuzzy neural networks
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An Improved Model Combining Evolutionary Algorithm and Neural Networks for PV Maximum Power Point Tracking
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Mechanical Performance and Microstructure Prediction of Hypereutectoid Rail Steels Based on BP Neural Networks
inferredcites →https://doi.org/10.1109/tfuzz.2019.2931870openalex-snapshot
Data-Knowledge-Based Fuzzy Neural Network for Nonlinear System Identification
inferredcites →https://doi.org/10.1109/tnnls.2019.2919441openalex-snapshot
RBFNN-Based Data-Driven Predictive Iterative Learning Control for Nonaffine Nonlinear Systems
inferredcites →https://doi.org/10.1179/026708306x81423openalex-snapshot
Empirical modelling of the isothermal transformation of pearlite in hypereutectoid steel
inferredcites →https://doi.org/10.3390/app10010051openalex-snapshot
A Back Propagation Neural Network Model Optimized by Mind Evolutionary Algorithm for Estimating Cd, Cr, and Pb Concentrations in Soils Using Vis-NIR Diffuse Reflectance Spectroscopy

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Experimental and machine learning approaches to phase prediction in high entropy alloy and medium entropy alloy
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Machine learning discovery of a new cobalt free multi-principal-element alloy with excellent mechanical properties
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Weighted Centroid Localization Algorithm Based on MEA-BP Neural Network and DBSCAN Clustering
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