Laser Powder Bed Fusion of Defect-Free NiTi Shape Memory Alloy Parts with Superior Tensile Superelasticity
Source abstract
Abstract
Laser powder bed fusion is a promising additive manufacturing technique for the fabrication of NiTi shape memory alloy parts with complex geometries that are otherwise difficult to fabricate through traditional processing methods. The technique is particularly attractive for the biomedical applications of NiTi shape memory alloys, such as stents, implants, and dental and surgical devices, where primarily the superelastic effect is exploited. However, few additively manufactured NiTi parts have been reported to exhibit superelasticity under tension in the as-printed condition, without a post-fabrication heat treatment, due to either persistent porosity formation or brittleness from oxidation during printing, or both. In this study, NiTi parts were fabricated using laser powder bed fusion and consistently exhibited room temperature tensile superelasticity up to 6% in the as-printed condition, almost twice the maximum reported value in the literature. This was achieved by eliminating porosity and cracks through the use of optimized processing parameters, carefully tailoring the evaporation of Ni from a Ni-rich NiTi powder feedstock, and controlling the printing chamber oxygen content. Crystallographic texture analysis demonstrated that the as-printed NiTi parts had a strong preferential texture for superelasticity, a factor that needs to be carefully considered when complex shaped parts are to be subjected to combined loadings. Transmission electron microscopy investigations revealed the presence of nano-sized oxide particles and Ni-rich precipitates in the as-printed parts, which play a role in the improved superelasticity by suppressing inelastic accommodation mechanisms for martensitic transformation.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Shape Memory Alloy Transformations
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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