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A novel approach has been developed for quantitative evaluation of the susceptibility of steels and alloys to hydrogen embrittlement. The approach uses a combination of hydrogen thermal desorption spectroscopy (TDS) analysis with recent advances in machine learning technology to develop a regression artificial neural network (ANN) model predicting hydrogen-induced degradation of mechanical properties of steels. We describe the thermal desorption data processing, artificial neural network architecture development, and the learning process beneficial for the accuracy of the developed artificial neural network model. A data augmentation procedure was proposed to increase the diversity of the input data and improve the generalization of the model. The study of the relationship between thermal desorption spectroscopy data and the mechanical properties of steel evidences a strong correlation of their corresponding parameters. A prototype software application based on the developed model is introduced and is openly available. The developed prototype based on TDS analysis coupled with ANN is shown to be a valuable engineering tool for steel characterization and quantitative prediction of the degradation of steel properties caused by hydrogen.
|Number of pages||14|
|Publication status||Published - 2 Dec 2020|
|MoE publication type||A1 Journal article-refereed|
- Artificial neural network
- Hydrogen embrittlement
- Hydrogen sensitivity
- Thermal desorption spectroscopy
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EARLY: New high-resolution non-destructive methods for assessment of early damage in advanced welded steels for high-temperature applications with extended life
Vilaça, P., Auerkari, P., Pohja, R., Malitckii, E., Santos Silva, M. & Kärki, O.
01/01/2020 → 31/12/2023
Project: Academy of Finland: Other research funding
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