Modeling high-temperature mechanical properties of austenitic stainless steels by neural networks

• Published in: Computational materials science Vol. 179; p. 109617
• Main Authors: Narayana, P.L., Lee, Sang Won, Park, Chan Hee, Yeom, Jong-Taek, Hong, Jae-Keun, Maurya, A.K., Reddy, N. S.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15-06-2020
• Subjects: Artificial neural networks; Austenitic stainless steels; Graphical user interface; Index of the relative importance; Property prediction; Index of the relative importance; Austenitic stainless steels; Artificial neural networks; Graphical user interface; Property prediction
• ISSN: 0927-0256; 1879-0801
• DOI: 10.1016/j.commatsci.2020.109617

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•Tensile properties of 18Cr-12Ni-Mo steels were modeled using neural networks.•Graphical user interface (GUI) was developed for easy use.•The model was able to correlate the relationships between input-output variables.•A virtual 18Cr-12Ni-Mo steels were created with mean values of the databases.•Effect of temperature and composition on properties was estimated accurately.
An artificial neural network (ANN) model was designed to correlate the complex relations among composition, temperature, and mechanical properties of 18Cr-12Ni-Mo austenitic stainless steels. The developed model was used to estimate the composition-property and temperature-property correlations with 97% and 91% accuracy, for train and unseen test datasets. The ANN predictions are more accurate with experimental results as compared with the calculated properties of the existing model. The effective response of the alloying elements on the mechanical properties at ambient as well as elevated temperatures was quantitatively estimated with the help of the index of relative importance (IRI). The calculated results of the ANN model beneficial for both researchers as well as designers to guide actual experiments. Hence, this proposed technique will be helpful in developing the components of austenitic stainless steel with desired properties.