Role of Hydrogen in the Separation of Interfaces in S13Cr Supermartensitic Stainless Steel
Source abstract
Abstract
Abstract This paper outlines experimental procedures and numerical analyses to investigate hydrogen embrittlement (HE) of S13Cr supermartensitic stainless steel (SMSS) subjected to various cathodic potentials. The hydrogen diffusion behavior was investigated using two electrochemical permeation techniques, namely, the double potentiostatic method (DPM) and the step method (SM), along with thermal desorption spectroscopy (TDS) tests. The apparent hydrogen diffusion coefficients varied from 1.4 × 10−13 to 4.7 × 10−12 m2/s, and TDS revealed the existence of deep traps, such as interfaces around precipitates and between retained austenite and ferritic matrix. The effects of HE in terms of a reduction in ductility were analyzed through tensile tests and fractographic analysis. A maximum reduction in elongation of approximately 14% was measured, and a majority of brittle fracture along the entire net section was observed in test samples pre-charged under -1.5 V/SCE and -1.7 V/SCE. A computational simulation was performed using the finite element method to predict the loss of ductility using the obtained experimental data. The computational model used a fracture-controlled method under static structural conditions, which links the reduction in elongation of the tensile specimens to the decrease in critical fracture energies, which, in turn, were calculated through a new mean field approach that used thermodynamic excess variables. The observations presented here suggest that S13Cr has good resistance to HE and that the computational model is reliable because only slight deviations in the magnitude (5%) were observed between the experimental and numerical results.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Hydrogen embrittlement and corrosion behaviors in metals · Corrosion Behavior and Inhibition · Fatigue and fracture mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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