Enhancing the Mechanical Properties of Cu-Al-Ni Shape Memory Alloys Locally Reinforced by Alumina through the Powder Bed Fusion Process

A classical problem with Cu-based shape memory alloys (SMAs) is brittle fracture at triple junctions. This alloy possesses a martensite structure at room temperature and usually comprises elongated variants. Previous studies have shown that introducing reinforcement into the matrix can refine grains and break martensite variants. Grain refinement diminishes brittle fracture at triple junctions, whereas breaking the martensite variants can negatively affect the shape memory effect (SME), owing to martensite stabilization. Furthermore, the additive element may coarsen the grains under certain circumstances if the material has a lower thermal conductivity than the matrix, even when a small amount is distributed in the composite. Powder bed fusion is a favorable approach that allows the creation of intricate structures. In this study, Cu-Al-Ni SMA samples were locally reinforced with alumina (Al2O3), which has excellent biocompatibility and inherent hardness. The reinforcement layer was composed of 0.3 and 0.9 wt% Al2O3 mixed with a Cu-Al-Ni matrix, deposited around the neutral plane within the built parts. Two different thicknesses of the deposited layers were investigated, revealing that the failure mode during compression was strongly influenced by the thickness and reinforcement content. The optimized failure mode led to an increase in fracture strain, and therefore, a better SME of the sample, which was locally reinforced by 0.3 wt% alumina under a thicker reinforcement layer.

Automated summary

This study investigates the problem of brittle fracture at triple junctions in Cu-based shape memory alloys and proposes a solution by locally reinforcing the alloy with alumina. The results show that the addition of alumina can refine grains and break martensite variants, reducing brittle fracture. However, the addition of the element may also coarsen grains and negatively affect the shape memory effect. The thickness and reinforcement content of the deposited layers strongly influence the failure mode, with a thicker reinforcement layer and 0.3 wt% alumina content leading to an optimized failure mode and improved shape memory effect.