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

Daniele Baraldi, Stefan Holmström, Karl-Fredrik Nilsson, Matthias Bruchhausen, Igor Simovovski

    Research outputpeer-review


    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
    Original languageEnglish
    Pages (from-to)1-24
    Number of pages24
    Issue number5
    StatePublished - 24 Apr 2021

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