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RESEARCH PAPER · Journal of Materials Science · 2020

Development of Vickers hardness prediction models via microstructural analysis and machine learning

Sucheta Swetlana, Nikhil Khatavkar, Abhishek Kumar Singh

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42 direct citing works

Development of Vickers hardness prediction models via microstructural analysis and machine learningOriginalModelling and optimization of microhardness of electroless Ni–P–TiO2 composite coating based on machine learning approaches and RSM82Transregional spatial correlation revealed by deep learning and implications for material characterisation and reconstruction21Accelerated Discovery of the Valley-Polarized Quantum Anomalous Hall Effect in MXenes31Feature Blending: An Approach toward Generalized Machine Learning Models for Property Prediction19Data-Driven Phase Selection, Property Prediction and Force-Field Development in Multi-Principal Element Alloys9Machine learning-accelerated first-principles predictions of the stability and mechanical properties of L12-strengthened cobalt-based superalloys21A data-driven machine learning approach to predict the hardenability curve of boron steels and assist alloy design27Lossless multi-scale constitutive elastic relations with artificial intelligence52Design of Ni-based turbine disc superalloys with improved yield strength using machine learning18Deep Learning to Predict Structure-Property Relationships of Polymer Blends1Application of explainable artificial intelligence for prediction and feature analysis of carbon diffusivity in austenite8DeepXRD, a Deep Learning Model for Predicting XRD spectrum from Material Composition34Surface Modification of AH36 Steel Using ENi-P-nano TiO2 Composite Coatings Through ANN-Based Modelling and Prediction6Machine-Learning-Based Predictions of Polymer and Postconsumer Recycled Polymer Properties: A Comprehensive Review62A micromechanics-based machine learning model for evaluating the microstructure-dependent rolling contact fatigue performance of a martensitic steel57Prediction of wear performance of ZK60 / CeO2 composites using machine learning models86Vickers Hardness Value Test via Multi-Task Learning Convolutional Neural Networks and Image Augmentation38Highly interpretable machine learning framework for prediction of mechanical properties of nickel based superalloys13Preparation and properties of artificial bone with lead-free piezoelectric materials4Machine learning assisted interpretation of creep and fatigue life in titanium alloys14Automatic Identification of the Multiphase Microstructures of Steels based on ASPP‐FCN4ANN for design and fabrication of ENi-P-nano TiO 2 coatings on AH36 steel5Fabrication, Experimental Investigation and Prediction of Wear Behavior of Open-Cell AlSi10Mg-SiC Composite Materials11High throughput calculations for a dataset of bilayer materials19COF-RF-Tool: A Python software for predicting the coefficient of friction of open-cell AlSi10Mg-SiC composites using Random Forest model11Design of novel Ni-based superalloys with better oxidation resistance with the aid of machine learning29Composition, heat treatment, microstructure and loading condition based machine learning prediction of creep life of superalloys26HardnessTesterV: A Web Machine Learning application for Vickers Hardness Prediction of a Metallic Alloy Using Flask API0Exploration into the microstructural, mechanical, and biological characteristics of the functionally graded 3Y-TZP/Ti6Al4V system as a potential material for dental implants12Advances in materials informatics: a review42Informatics-Driven Design of Superhard B–C–O Compounds4Combined approach to capture the evolution of oxidation of Nickel based superalloys using data driven approaches7Predictive Modeling of Vickers Hardness Using Machine Learning Techniques on D2 Steel with Various Treatments18Wear on steel tillage tools: A review of material, soil and dynamic conditions35Critical Velocity for Cold-Sprayed Coatings4Chemistry and Local Environment Adaptive Representation graphs as material descriptors10Aluminum Alloy Design by La Amount through Machine Learning and Experimental Verification2Frictional Properties of Two-Dimensional Materials: Data-Driven Machine Learning Predictive Modeling16Machine learning approaches for predicting mechanical properties in additive manufactured lattice structures34Facilitated the discovery of new γ/γ′ Co-based superalloys by combining first-principles and machine learning16Microstructural Evolution and Kinetics of 7075 Al Alloy During Homogenization Treatment4Machine learning prediction of materials properties from chemical composition: Status and prospects29Solution heat treatment of support-free IN625 overhang sections fabricated via laser powder bed fusion (LPBF)3Design of novel interpretable deep learning framework for microstructure–property relationships in nickel and cobalt based superalloys11A Qualitative Correlation Between Microstructure and Tribological Responses of Ni-Based Alloys Under Complex Working Conditions0Machine learning-based prediction of wear behaviour of AZ31 hybrid composites reinforced with NbC and ZrC8Machine Learning Based Prediction of Biomaterial Hardness for Medical Devices0Machine learning models based wear performance prediction of AZ31/TiC composites5Research Progress on Prediction of Mechanical Properties of Materials Based on Materials Genome Engineering0Hardenability prediction model of gear steel via constrained partial least squares regression0

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  2. Transregional spatial correlation revealed by deep learning and implications for material characterisation and reconstruction

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  3. Accelerated Discovery of the Valley-Polarized Quantum Anomalous Hall Effect in MXenes

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  8. Lossless multi-scale constitutive elastic relations with artificial intelligence

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  15. A micromechanics-based machine learning model for evaluating the microstructure-dependent rolling contact fatigue performance of a martensitic steel

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  16. Prediction of wear performance of ZK60 / CeO2 composites using machine learning models

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  17. Vickers Hardness Value Test via Multi-Task Learning Convolutional Neural Networks and Image Augmentation

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  18. Highly interpretable machine learning framework for prediction of mechanical properties of nickel based superalloys

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  19. Preparation and properties of artificial bone with lead-free piezoelectric materials

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  20. Machine learning assisted interpretation of creep and fatigue life in titanium alloys

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  21. Automatic Identification of the Multiphase Microstructures of Steels based on ASPP‐FCN

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