DataVXJournal of Materials SciencePaper
journal article·2023-02

Influence of friction stir processing on microstructure, mechanical properties and corrosion behaviour of Mg-Zn-Dy alloy

Uzwalkiran Rokkala, Srikanth Bontha, M. R. Ramesh, Vamsi Krishna Balla

Journal of Materials Science· Volume 58· Issue 6
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Source abstract

Abstract

In the present study, friction stir processing (FSP) was carried out on as-cast Mg-Zn-Dy alloy to tailor grain size and texture which alter the mechanical properties and corrosion behaviour. The grain size of the as-cast alloy was reduced from 60 ± 2 µm to 3 ± 0.1 µm after FSP due to dynamic recrystallization. The effect of grain size, crystallographic orientation and fine precipitates on mechanical properties were investigated using field emission scanning electron microscope (FESEM) and electron back scattered diffraction (EBSD). The ultimate tensile strength, yield strength, % elongation and hardness of FSPed alloy improved by 55%, 60%, 53% and 46% when compared to as-cast alloy. The FSPed Mg-Zn-Dy alloy exhibited a 79% decrease in corrosion rate when compared to as-cast alloy which can be attributed to grain refinement, uniform distribution of secondary precipitates and strong basal texture. The surface of FSPed sample after immersion corrosion exhibited calcium phosphate rich minerals which help in apatite formation on the sample surface. Cytotoxicity studies using MTT assay revealed more than 80% cell viability for both as-cast and FSPed alloy illustrating non-toxic nature of both the samples. The results of this study indicate that FSPed Mg-Zn-Dy alloy is a potential material for biodegradable implants due to its high strength, corrosion resistance and biocompatibility.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

Metals & alloysBiomaterials

OpenAlex topics: Magnesium Alloys: Properties and Applications · Aluminum Alloys Composites Properties · Advanced Welding Techniques Analysis

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

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