316L(N) Creep modeling with phenomenological approach and artificial intelligence based methods

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316L(N) Creep modeling with phenomenological approach and artificial intelligence based methods. / Baraldi, Daniele; Holmström, Stefan; Nilsson, Karl-Fredrik; Bruchhausen, Matthias; Simovovski, Igor.

In: Metals, Vol. 11, No. 5, 24.04.2021, p. 1-24.

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Baraldi, D, Holmström, S, Nilsson, K-F, Bruchhausen, M & Simovovski, I 2021, '316L(N) Creep modeling with phenomenological approach and artificial intelligence based methods', Metals, vol. 11, no. 5, pp. 1-24. https://doi.org/10.3390/met11050698

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Baraldi, Daniele ; Holmström, Stefan ; Nilsson, Karl-Fredrik ; Bruchhausen, Matthias ; Simovovski, Igor. / 316L(N) Creep modeling with phenomenological approach and artificial intelligence based methods. In: Metals. 2021 ; Vol. 11, No. 5. pp. 1-24.

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@article{1ba1a7bf07644ab5832da1c1c17eb651,
title = "316L(N) Creep modeling with phenomenological approach and artificial intelligence based methods",
abstract = "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) steel—i.e., primary, secondary, and tertiary creep. The processed design data were used to develop three models with different approaches for the creep rate: a phenomenological approach; an artificial neural network; and an artificial intelligence method based on symbolic regression and genetic programming. It was shown that all three models are capable of describing the true creep rate as a function of true creep strain and true stress over a wide range of engineering stresses and temperatures without the need of additional micro-structural information. Furthermore, the results of finite element simulations reproduce the trends of experimental data from the literature Special Issue: https://www.mdpi.com/journal/metals/special_issues/creep_deformation_elevated_temperatures",
keywords = "Creep model, 316L(N), LSCP model, Neural network, Machine learning, Phenomenological approach, Austenitic stainless steel",
author = "Daniele Baraldi and Stefan Holmstr{\"o}m and Karl-Fredrik Nilsson and Matthias Bruchhausen and Igor Simovovski",
note = "Score=10",
year = "2021",
month = "4",
day = "24",
doi = "10.3390/met11050698",
language = "English",
volume = "11",
pages = "1--24",
journal = "Metals",
issn = "2075-4701",
publisher = "MDPI",
number = "5",

}

RIS - Download

TY - JOUR

T1 - 316L(N) Creep modeling with phenomenological approach and artificial intelligence based methods

AU - Baraldi, Daniele

AU - Holmström, Stefan

AU - Nilsson, Karl-Fredrik

AU - Bruchhausen, Matthias

AU - Simovovski, Igor

N1 - Score=10

PY - 2021/4/24

Y1 - 2021/4/24

N2 - 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) steel—i.e., primary, secondary, and tertiary creep. The processed design data were used to develop three models with different approaches for the creep rate: a phenomenological approach; an artificial neural network; and an artificial intelligence method based on symbolic regression and genetic programming. It was shown that all three models are capable of describing the true creep rate as a function of true creep strain and true stress over a wide range of engineering stresses and temperatures without the need of additional micro-structural information. Furthermore, the results of finite element simulations reproduce the trends of experimental data from the literature Special Issue: https://www.mdpi.com/journal/metals/special_issues/creep_deformation_elevated_temperatures

AB - 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) steel—i.e., primary, secondary, and tertiary creep. The processed design data were used to develop three models with different approaches for the creep rate: a phenomenological approach; an artificial neural network; and an artificial intelligence method based on symbolic regression and genetic programming. It was shown that all three models are capable of describing the true creep rate as a function of true creep strain and true stress over a wide range of engineering stresses and temperatures without the need of additional micro-structural information. Furthermore, the results of finite element simulations reproduce the trends of experimental data from the literature Special Issue: https://www.mdpi.com/journal/metals/special_issues/creep_deformation_elevated_temperatures

KW - Creep model

KW - 316L(N)

KW - LSCP model

KW - Neural network

KW - Machine learning

KW - Phenomenological approach

KW - Austenitic stainless steel

UR - https://ecm.sckcen.be/OTCS/llisapi.dll/open/43880398

U2 - 10.3390/met11050698

DO - 10.3390/met11050698

M3 - Article

VL - 11

SP - 1

EP - 24

JO - Metals

JF - Metals

SN - 2075-4701

IS - 5

ER -

ID: 7126268