Effects of Al-Si coating structures on bendability and resistance to hydrogen embrittlement in 1.5-GPa-grade hot-press-forming steel
Source abstract
Abstract
Hot-press-forming (HPF) steels have attracted great attention as automotive reinforcement parts, but are exposed to the potential risk of hydrogen embrittlement (HE) because H introduced easily during HPF processes is hardly de-trapped through the solidified coating. In particular, H seriously deteriorates bendability, which is one of the main properties to be considered. In this study, the Al-Si coating structures were modified to improve H emission by increasing H diffusivity. The effects of coating structures on bendability and H desorption were investigated by interrupted bending tests, H-permeation tests, and thermal desorption analyses according to elapsed time after H-charging. Immersion in an Al-10%Si bath and the subsequent HPF process (930 °C for 6 min) produced a 33 μm-thick multiple coating structure composed of Fe 2Al 5 , FeAl, and ferrite layers. On the other hand, the reduced Al-Si adhesion amount from the dip bath and the increased time and temperature (950 °C for 30 min) produced a 30 µm-thick body-centered-cubic (BCC)-based coating structure composed of FeAl and ferrite layers. The BCC-based crystal structure, reduced Al content in the FeAl layer, and coarsened ferrite grains effectively enhanced H diffusivity and suppressed H-induced degradation. Moreover, the softened FeAl and thick ferrite layers improved bendability by allowing the large strain accommodation of bending deformation. Thus, this work proposes an optimal Al-Si coating design that enhances both bendability and resistance to H-induced degradation for secure HPF steel applications.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Hydrogen embrittlement and corrosion behaviors in metals · Microstructure and Mechanical Properties of Steels · Metal and Thin Film Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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