Hydrogen gaseous embrittlement effect over mechanical properties of an experimental X-120 microalloyed steel subjected to heat treatments and different cooling rates

Villalobos, Julio C.; Del-Pozo, Adrian; Vergara–Hernández, Héctor Javier; Vázquez–Gómez, Octavio; Escudero-García, Carlos F.; Serna, S.; Campillo, B.

doi:10.1016/j.ijhydene.2022.07.042

Cited by 3 publications

(2 citation statements)

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“…The normalized flux results are shown below, as well as their fitted interpretation for better comprehension, in Figure 8b. Finally, applying Equation ( 9) [32][33][34][35], the trap density (N T ) was calculated. The used parameters values were D eff extracted from the experimental data, D L = 2.31 × 10 −14 m 2 /s, Eb = 0.2 eV [50], and the K temperature and R gas constant were considered.…”

Section: Permeation Testsmentioning

confidence: 99%

“…Regarding the above, due to the presence of defects, second-phase particles, and in general, the microstructural state of the superalloy, the D eff and J ss , can be complemented with the trap density calculation (N T ), obtaining values for reversible and irreversible sites. Thus, several models have been proposed, such as the McNabb-Foster, Oriani, and Dong et al, among others [23,24,[32][33][34][35]. On the other hand, combining results of the material characterization techniques, H permeability evaluations, and mathematical modeling for H trapping, the application of artificial intelligence can be an excellent choice to develop a useful predictive model.…”

Section: Introductionmentioning

confidence: 99%

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Hydrogen Diffusion in Nickel Superalloys: Electrochemical Permeation Study and Computational AI Predictive Modeling

Román-Sedano,

Campillo,

Villalobos

et al. 2023

Materials

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Ni-based superalloys are materials utilized in high-performance services that demand excellent corrosion resistance and mechanical properties. Its usages can include fuel storage, gas turbines, petrochemistry, and nuclear reactor components, among others. On the other hand, hydrogen (H), in contact with metallic materials, can cause a phenomenon known as hydrogen embrittlement (HE), and its study related to the superalloys is fundamental. This is related to the analysis of the solubility, diffusivity, and permeability of H and its interaction with the bulk, second-phase particles, grain boundaries, precipitates, and dislocation networks. The aim of this work was mainly to study the effect of chromium (Cr) content on H diffusivity in Ni-based superalloys; additionally, the development of predictive models using artificial intelligence. For this purpose, the permeability test was employed based on the double cell experiment proposed by Devanathan–Stachurski, obtaining the effective diffusion coefficient (Deff), steady-state flux (Jss), and the trap density (NT) for the commercial and experimentally designed and manufactured Ni-based superalloys. The material was characterized with energy-dispersed X-ray spectroscopy (EDS), atomic absorption, CHNS/O chemical analysis, X-ray diffraction (XRD), brightfield optical microscopy (OM), and scanning electron microscopy (SEM). On the other hand, predictive models were developed employing artificial neural networks (ANNs) using experimental results as a database. Furthermore, the relative importance of the main parameters related to the H diffusion was calculated. The Deff, Jss, and NT achieved showed relatively higher values considering those reported for Ni alloys and were found in the following orders of magnitude: [1 × 10−8, 1 × 10−11 m2/s], [1 × 10−5, 9 × 10−7 mol/cm2s], and [7 × 1025 traps/m3], respectively. Regarding the predictive models, linear correlation coefficients of 0.96 and 0.80 were reached, corresponding to the Deff and Jss. Due to the results obtained, it was suitable to dismiss the effect of Cr in solid solution on the H diffusion. Finally, the predictive models developed can be considered for the estimation of Deff and Jss as functions of the characterized features.

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