journal article·2024-06

Trade-off between local chemical order and lattice distortion in affecting dislocation motion in NbTiZr multi-principal element alloys

Bing Chen, Suzhi Li, Jun Ding, Xiangdong Ding, Jun Sun, En Ma

Acta Materialia· Volume 272
Publisher page

Abstract

Local chemical order (LCO) is energetically favorable in multi-principal element alloys (MPEAs) due to the chemical affinity difference among constituent species. Here we show that the increase of LCO is often accompanied by a decrease of lattice distortion in body-centered-cubic (BCC) MPEAs. This leads to a trade-off between LCO and lattice distortion in their effects on retarding the glide of edge and screw dislocations. With a random solid solution without LCO as a reference, the emergence of LCO at the expense of lattice distortion could weaken the local trapping force for dislocation glide and promote dislocation mobility. However, when the LCO becomes sufficiently large and robust, it acts to hinder dislocation glide. We found that the mobility of edge dislocation is more affected by local lattice distortion, while the kink-pair based screw dislocation motion is more governed by LCO. We further quantitatively evaluate the contribution of LCO and lattice distortion to the overall critical shear stress for dislocation glide using analytical models. For the present equiatomic NbTiZr MPEAs, the critical shear stress is dominated by lattice distortion when the LCO is still limited to chemical short-range order (CSRO). However, LCO could even surpass lattice distortion to become a major contributor to the critical shear stress once it grows to a sufficiently large size. Our work indicates that increasing the degree and extent of LCO may provide an effective means to regulate dislocation motion, shedding light on how to use LCOs to improve the strength of BCC MPEAs.
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Research context

OpenAlex topics: Intermetallics and Advanced Alloy Properties · Metal and Thin Film Mechanics · High Entropy Alloys Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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