Study of correlation between the steels susceptibility to hydrogen embrittlement and hydrogen thermal desorption spectroscopy using artificial neural network

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2020-09-01
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Mcode
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Language
en
Pages
12
14995-15006
Series
Neural Computing & Applications, Volume 32, issue 18
Abstract
Steels are the most used structural material in the world, and hydrogen content and localization within the microstructure play an important role in its properties, namely inducing some level of embrittlement. The characterization of the steels susceptibility to hydrogen embrittlement (HE) is a complex task requiring always a broad and multidisciplinary approach. The target of the present work is to introduce the artificial neural network (ANN) computing system to predict the hydrogen-induced mechanical properties degradation using the hydrogen thermal desorption spectroscopy (TDS) data of the studied steel. Hydrogen sensitivity parameter (HSP) calculated from the reduction of elongation to fracture caused by hydrogen was linked to the corresponding hydrogen thermal desorption spectra measured for austenitic, ferritic, and ferritic-martensitic steel grades. Correlation between the TDS input data and HSP output data was studied using two ANN models. A correlation of 98% was obtained between the experimentally measured HSP values and HSP values predicted using the developed densely connected layers ANN model. The performance of the developed ANN models is good even for never-before-seen steels. The ANN-coupled system based on the TDS is a powerful tool in steels characterization especially in the analysis of the steels susceptibility to HE.
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Keywords
Hydrogen embrittlement, Artificial neural network, Thermal desorption spectroscopy, Hydrogen sensitivity parameter, MECHANICAL-PROPERTIES, RETAINED AUSTENITE, BEHAVIOR, FRACTURE
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Citation
Malitckii , E , Fangnon , E & Vilaca , P 2020 , ' Study of correlation between the steels susceptibility to hydrogen embrittlement and hydrogen thermal desorption spectroscopy using artificial neural network ' , Neural Computing & Applications , vol. 32 , no. 18 , pp. 14995-15006 . https://doi.org/10.1007/s00521-020-04853-3