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

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@Article{baraldi:2021:Metals,

• author = "Daniele Baraldi and Stefan Holmstrom and Karl-Fredrik Nilsson and Matthias Bruchhausen and Igor Simonovski",
• title = "{316L(N)} Creep Modeling with Phenomenological Approach and Artificial Intelligence Based Methods",
• journal = "Metals",
• year = "2021",
• volume = "11",
• number = "5",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2075-4701",
• URL = "https://www.mdpi.com/2075-4701/11/5/698",
• DOI = "doi:10.3390/met11050698",
• abstract = "A model that describes creep behaviour 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.",
• notes = "also known as \cite{met11050698}",

}

Genetic Programming entries for Daniele Baraldi Stefan Holmstrom Karl-Fredrik Nilsson Matthias Bruchhausen Igor Simonovski

Citations