University of Oulu

Eric Fangnon, Evgenii Malitckii, Renata Latypova, Pedro Vilaça, Prediction of hydrogen concentration responsible for hydrogen-induced mechanical failure in martensitic high-strength steels, International Journal of Hydrogen Energy, 2022, ISSN 0360-3199, https://doi.org/10.1016/j.ijhydene.2022.11.151

Prediction of hydrogen concentration responsible for hydrogen-induced mechanical failure in martensitic high-strength steels

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Author: Fangnon, Eric1; Malitckii, Evgenii1; Latypova, Renata2;
Organizations: 1Department of Mechanical Engineering, School of Engineering, Aalto University, P.O Box 11000, FI-00076, Espoo, Finland
2Faculty of Technology, Materials and Mechanical Engineering, Centre for Advanced Steels Research (CASR), University of Oulu, P.O Box 4200, FI-90014, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 3.3 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202301091954
Language: English
Published: Elsevier, 2022
Publish Date: 2023-01-09
Description:

Abstract

Hydrogen, at critical concentrations, responsible for hydrogen-induced mechanical property degradation cannot yet be estimated beforehand and can only be measured experimentally upon fracture with specific specimen sizes. In this work, we develop two deep learning artificial neural network (ANN) models with the ability to predict hydrogen concentration responsible for early mechanical failure in martensitic ultra-high-strength steels. This family of steels is represented by four different steels encompassing different chemical compositions and heat treatments. The mechanical properties of these steels with varying size and morphology of prior austenitic grains in as-supplied state and after hydrogen-induced failure together with their corresponding hydrogen charging conditions were used as inputs. The feed forward back propagation models with network topologies of 12-7-5-3-2-1 (I) and 14-7-5-3-2-1 (II) were validated and tested with unfamiliar data inputs. The models I and II show good hydrogen concentration prediction capabilities with mean absolute errors of 0.28, and 0.33 wt.ppm at test datasets, respectively. A linear correlation of 80% and 77%, between the experimentally measured and ANN predicted hydrogen concentrations, was obtained for Model I and II respectively. This shows that for this family of steels, the estimation of hydrogen concentration versus property degradation is a feasible approach for material safety analysis.

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Series: International journal of hydrogen energy
ISSN: 0360-3199
ISSN-E: 0360-3199
ISSN-L: 0360-3199
Issue: Online first
DOI: 10.1016/j.ijhydene.2022.11.151
OADOI: https://oadoi.org/10.1016/j.ijhydene.2022.11.151
Type of Publication: A1 Journal article – refereed
Field of Science: 216 Materials engineering
Subjects:
Funding: This researcher was supported by the Public Research Networked with Companies (Co-Innovation) program of Business Finland via the projects 7743/31/2018 (ISA Aalto-HydroSafeSteels) and 7537/31/2018 (ISA-Intelligent Steel Applications).
Copyright information: © 2022 The Author(s). Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
  https://creativecommons.org/licenses/by/4.0/