316L(N) Creep Modeling with Phenomenological Approach and Artificial Intelligence Based Methods

316L(N) Creep Modeling with Phenomenological Approach and Artificial Intelligence Based Methods

A model that describes creep behavior is essential in the design or life assessment of components and systems that operate at high temperatures. Using the RCC-MRx data and the LCSP (logistic creep strain prediction) model, processed design data were generated over the whole creep regime of 316L(N) s...

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Bibliographic Details
Main Authors: Daniele Baraldi, Stefan Holmström, Karl-Fredrik Nilsson, Matthias Bruchhausen, Igor Simonovski
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Metals
Subjects:
creep model
316L(N)
LSCP model
neural network
machine learning
phenomenological approach
Online Access:https://www.mdpi.com/2075-4701/11/5/698

Internet

https://www.mdpi.com/2075-4701/11/5/698

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