Salim Brahimi | Searcharxiv

arXiv2025

Hydrogen trapping in sub-stoichiometric niobium and vanadium carbide precipitates in high-strength steels

High-strength steel is a structural metal crucial for load-bearing components yet is known to be highly susceptible to hydrogen embrittlement (HE). Vanadium (V) and niobium (Nb) containing precipitated carbides introduce strong hydrogen traps to immobilize hydrogen, thus mitigating HE. However, variations in intrinsic vacancy concentrations in these carbides affect hydrogen thermodynamics and kinetics but remain poorly understood. Employing first-principles calculations, hydrogen trapping and diffusion in V/Nb carbides were investigated. Hydrogen dissolution energies are composition-dependent, revealing a transition from reversible to irreversible trapping with increasing carbon vacancy content, prescribed by the strength of covalent bonds with neighboring V/Nb atoms. Meanwhile, the diffusion energy barrier decreases with increasing carbon vacancy content, attributed to changes in vacancy patterns within carbides. The findings contribute new and critical knowledge for understanding hydrogen trapping and diffusion in sub-stoichiometric V/Nb carbides, providing valuable guidance for process and composition innovation of high-strength alloy steels for better HE resistance.

cond-mat.mtrl-sci↗

arXiv2023

MatSegNet: a New Boundary-aware Deep Learning Model for Accurate Carbide Precipitate Analysis in High-Strength Steels

Lower Bainite (LB) and Tempered Martensite (TM) are two common microstructures in modern high-strength steels. LB and TM can render similar mechanical properties for steels, yet LB is often considered superior to TM in resistance to hydrogen embrittlement. Such performance difference has conventionally been attributed to their distinction in certain microstructural features, particularly carbides. The present study developed, MatSegNet, a new contour-aware deep learning (DL) architecture. It is tailored for comprehensive segmentation and quantitative characterization of carbide precipitates with complex contours in high-strength steels, shown to outperform existing state-of-the-art DL architectures. Based on MatSegNet, a high-throughput DL pipeline has been established for precise comparative carbide analysis in LB and TM. The results showed that statistically the two microstructures exhibit similarity in key carbide characteristics with marginal difference, cautioning against the conventional use of carbide orientation as a reliable means to differentiate LB and TM in practice. Through MatSegNet, this work demonstrated the potential of DL to play a critical role in enabling accurate and quantitative microstructure characterization to facilitate development of structure-property relationships for accelerating materials innovation.

cs.CV↗