Neural network modelling studies of steam oxidised kinetic behaviour of advanced steels and Ni-based alloys at 800 °C for 3000 h-Reference-Cited by-同舟云学术

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Neural network modelling studies of steam oxidised kinetic behaviour of advanced steels and Ni-based alloys at 800 °C for 3000 h

Published:2018-04 Issue: Volume:133 Page:94-111
ISSN:0010-938X
Container-title:Corrosion Science
language:en
Short-container-title:Corrosion Science

Author:

Dudziak T.,Gajewski P.,Śnieżyński B.,Deodeshmukh V.,Witkowska M.,Ratuszek W.,Chruściel K.

Funder

National Science Centre in Poland

Publisher

Elsevier BV

Subject

General Materials Science,General Chemical Engineering,General Chemistry

Reference44 articles.

1. Smarter, Greener, More Inclusive? Indicators to Support the Europe 2020 Strategy, Eurostat, Statistical Books,2015

2. Boiler materials for ultra-Supercritical coal power plants steamside oxidation;Viswanathan;J. Mater. Eng. Perform.,2006

3. Long-Term performance of high-temperature foil alloys in water vapor containing environment. Part I: oxidation behavior;Deodeshmukh;Oxid. Met.,2013

4. Longterm oxidation of newly developed HIPIMS and PVD coatings with neural network prediction modelling;Danaher;Corros. Sci.,2013

5. A combined neural network and mechanistic approach for the prediction of corrosion rate and yield strength of magnesium-rare earth alloys;Birbilis;Corros. Sci.,2011

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1. High-Temperature Steam Oxidation Behavior of VDM Alloy 699 XA Produced by Laser Powder Bed Fusion;Journal of Materials Engineering and Performance;2024-08-19

2. Al- and Ta-Modified Alloy 740 Superalloy Castings Produced in Non-vacuum Conditions—Microstructure and Selected Properties;Metallurgical and Materials Transactions A;2024-07-20

3. Artificial neural network modelling to predict the efficiency of aluminium sacrificial anode;Corrosion Engineering, Science and Technology;2023-09

4. Application Of Artificial Neural Network for Prediction of the Cyclic Oxidation Behavior of Electrical Resistance Sintered Gamma-TiAl Intermetallics;Archives of Metallurgy and Materials;2023-07-26

5. Experimental and neural networks analysis on elevated-temperature mechanical properties of structural steels;Materials Today Communications;2022-08

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