Quantification of hydrogen trapping in multiphase steels: Part I – Point traps in martensite
Source abstract
Abstract
We quantified systematically the H trap density in martensite resulting from the presence of dislocations, grain boundaries and retained austenite through a combination of detailed microstructural characterisation, H permeation, thermal desorption and diffusion modelling. This thorough analysis allowed for the first time to deconvolve key microstructural constituents affecting H diffusion in multi-trap martensite. Three microstructures were investigated – as-quenched, tempered at 300 °C and tempered at 450 °C. The first two microstructures had identical dislocation densities and grain size, while the as-quenched one also contained 3.5 vol.% of retained austenite. The two tempered microstructures showed a large difference in dislocation density with few other microstructural differences. The as-quenched microstructure exhibited over an order of magnitude lower H diffusivity and increased H trapping due to retained austenite, while the tempered samples exhibited very similar diffusivities, indicating that dislocations have a limited effect on H trapping. Trap density scaling laws were successfully identified and validated through diffusion simulations and experiments. It was also shown that in martensite and heavily deformed ferrite, where the average grain size is small, grain boundaries are more effective trapping sites than dislocations. Our results also show that retained austenite cannot be effectively modelled as a point trap under the local equilibrium assumption, which is frequently used to model its effect on H diffusion, and that bulk trapping must be considered at least in two dimensions, which is addressed in part II of this series.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Hydrogen embrittlement and corrosion behaviors in metals · Microstructure and Mechanical Properties of Steels · Corrosion Behavior and Inhibition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (77)
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