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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.
|Number of pages||13|
|Journal||International Journal of Hydrogen Energy|
|Early online date||30 Nov 2022|
|Publication status||Published - 15 Feb 2023|
|MoE publication type||A1 Journal article-refereed|
- Artificial neural network
- Deep learning
- Hydrogen concentration
- Hydrogen embrittlement
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- 1 Finished
ISA AALTO: HydroSafeSteels, Evaluation of the effects of hydrogen on the mechanical performance of modern high strength steels for demanding applications
Vilaça, P., Fangnon, A., Malitckii, E. & Yagodzinskyy, Y.
01/05/2019 → 31/12/2021
Project: Business Finland: Other research funding