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Modeling of Creep Deformation Behaviour of RAFM Steel
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/0921-5093(92)90290-hopenalex-snapshot
Prediction of creep parameters of type 316 stainless steel under service conditions using the π-projection concept
inferredcites →https://doi.org/10.1016/j.commatsci.2012.09.007openalex-snapshot
Modeling the creep behavior of 2024-T3 Al alloy
inferredcites →https://doi.org/10.1016/j.engfracmech.2008.06.017openalex-snapshot
Creep characterization of a Ni-based Hastelloy-X alloy by using theta projection method
inferredcites →https://doi.org/10.1016/j.fusengdes.2005.06.186openalex-snapshot
Present development status of EUROFER and ODS-EUROFER for application in blanket concepts
inferredcites →https://doi.org/10.1016/j.jnucmat.2011.01.058openalex-snapshot
Tensile and creep properties of reduced activation ferritic–martensitic steel for fusion energy application
inferredcites →https://doi.org/10.1016/j.msea.2008.06.057openalex-snapshot
Analysis of long-term creep curves by constitutive equations
inferredcites →https://doi.org/10.1016/j.msea.2011.10.112openalex-snapshot
Creep deformation and rupture behaviour of 9Cr–1W–0.2V–0.06Ta Reduced Activation Ferritic–Martensitic steel
inferredcites →https://doi.org/10.1016/j.proeng.2013.03.253openalex-snapshot
Effects of Tungsten and Tantalum on Creep Deformation and Rupture Properties of Reduced Activation Ferritic-Martensitic Steel
inferredcites →https://doi.org/10.1016/s0142-1123(03)00064-1openalex-snapshot
Low cycle fatigue and creep–fatigue interaction behavior of 316L(N) stainless steel and life prediction by artificial neural network approach
inferredcites →https://doi.org/10.1023/a:1016031907086openalex-snapshot
Sensitivity of the theta projection technique to the functional form of the theta interpolation/extrapolation function
inferredcites →https://doi.org/10.1111/j.1460-2695.2006.01066.xopenalex-snapshot
Creep deformation and rupture behaviour of directionally solidified GTD 111 superalloy
inferredcites →https://doi.org/10.1179/026708301101510276openalex-snapshot
Theθprojection method applied to small strain creep of commercial aluminium alloy
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s10854-017-7917-zopenalex-snapshot
Elevated-temperature shear creep evolution and life prediction of sintered nano-silver lap-shear joint
inferred← citeshttps://doi.org/10.1007/s12666-024-03532-4openalex-snapshot
A Meso-Scale Model to Describe the Creep Response and Microstructure Evolution of Superalloy IN-718
inferred← citeshttps://doi.org/10.1016/j.actamat.2025.121073openalex-snapshot
From creep-life prediction to ultra-creep-resistant steel design: An uncertainty-informed machine learning approach
inferred← citeshttps://doi.org/10.1016/j.finmec.2022.100117openalex-snapshot
An improved dislocation density reliant model to address the creep deformation of reduced activation ferritic martensitic steel
inferred← citeshttps://doi.org/10.1016/j.jmapro.2016.11.007openalex-snapshot
Experimental investigation on microstructure and mechanical properties of activated TIG welded reduced activation ferritic/martensitic steel joints
inferred← citeshttps://doi.org/10.1016/j.jmrt.2022.11.154openalex-snapshot
Application of soft constrained machine learning algorithms for creep rupture prediction of an austenitic heat resistant steel Sanicro 25
inferred← citeshttps://doi.org/10.1016/j.jmrt.2023.10.223openalex-snapshot
Evaluating creep rupture life in austenitic and martensitic steels with soft-constrained machine learning
inferred← citeshttps://doi.org/10.1016/j.matdes.2021.110326openalex-snapshot
High-throughput map design of creep life in low-alloy steels by integrating machine learning with a genetic algorithm
inferred← citeshttps://doi.org/10.1016/j.matpr.2019.12.417openalex-snapshot
On new relation for theta projection approach for the prediction of creep behaviour of alloy D9I austenitic stainless steel
inferred← citeshttps://doi.org/10.1016/j.matpr.2022.11.292openalex-snapshot
Modelling the creep curves of RAFM steel employing a dislocation density reliant model
inferred← citeshttps://doi.org/10.1016/j.mtcomm.2025.112467openalex-snapshot
High-throughput design strategy for creep-resistance steel using machine learning and optimization algorithm
inferred← citeshttps://doi.org/10.1088/2053-1591/aad65fopenalex-snapshot
Modeling the two-stage creep-aging behaviors of an Al-Cu-Mg alloy
inferred← citeshttps://doi.org/10.3390/met15020120openalex-snapshot
Data and Knowledge Dual-Driven Creep Life Prediction for Austenitic Heat-Resistance Steel
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