In this study, an optimal selective laser melting (SLM) process for manufacturing dense NiTi alloy with pseudoelasticity and shape-memory capability was proposed. The microstructure, phase-transformation temperature, shape memory capability, and pseudoelasticity were investigated by scanning electron microscopy, X-ray diffraction, differential scanning calorimetry, and bending and tensile tests. NiTi powder with a particle size > 45 μm was selected for the subsequent SLM process, because it exhibited a Ni/Ti ratio of ~1 and a lower oxygen content than powders with smaller particle sizes. A thin-walled disk (0.48 mm thick) and cuboid samples (5 mm thick) were prepared for investigating the variation in the homogeneity of the microstructure. The thin-walled SLM-NiTi sample exhibited a marginally inhomogeneous microstructure between layers, and defects existed in the previously formed side. The cuboid SLM-NiTi sample was fabricated without undesirable secondary phases, and it exhibited a 100% shape-recovery rate under 2% bending strain and completely pseudoelastic under 3% strain. Also, the SLM-NiTi exhibited lower phase-transformation temperatures and a broader phase-transformation range than the original NiTi. The phase-transformation range can be reduced by annealing.

Hardness testing is an essential test in the metal manufacturing industry, and Vickers hardness is one of the most widely used hardness measurements today. The computer-assisted Vickers hardness test requires manually generating indentations for measurement, but the process is tedious and the measured results may depend on the operator’s experience. In light of this, this paper proposes a data-driven approach based on convolutional neural networks to measure the Vickers hardness value directly from the image of the specimen to get rid of the aforementioned limitations. Multi-task learning is introduced in the proposed network to improve the accuracy of Vickers hardness measurement. The metal material used in this paper is medium-carbon chromium-molybdenum alloy steel (SCM 440), which is commonly utilized in automotive industries because of its corrosion resistance, high temperature, and tensile strength. However, the limited samples of SCM 440 and the tedious manual measurement procedure represent the main challenge to collect sufficient data for training and evaluation of the proposed methods. In this regard, this study introduces a new image mixing method to augment the dataset. The experimental results show that the mean absolute error between the Vickers hardness value output by the proposed network architecture can be 10.2 and the value can be further improved to 7.6 if the multi-task learning method is applied. Furthermore, the robustness of the proposed method is confirmed by evaluating the developed models with an additional 59 unseen images provided by specialists for testing, and the experimental results provide evidence to support the reliability and usability of the proposed methods.

The nickel-titanium alloy (57Ni-43Ti in wt%) was atomized by the plasma rotating electrode process (PREP). The PREP parameters such as plasma arc current, rotating electrode speed with corresponding PREP powder size range in weight percentage analysis, powder morphology and biocapability of cells were studied by scanning electron microscopies, Inductively Coupled Plasma and X-ray diffraction techniques. From the electrode of the produced powders, the composition has no obviously changes. Weight percentage up to 31.8% of the range under 300 μm while the rotation electrode speed increase to 12k rpm. Spherical and flat with smooth surface were observed in different size range. Brittle phase was not observed of XRD data. The nitinol powder has high biocapability with cells showed no cytotoxicity and well cell adhesion in the in vivo assay.

The anatase nanocondensates produced by energetic pulse laser ablation on Ti target under oxygen background gas have been observed by transmission electron microscopy. They were found to coalesce in approximately parallel orientation on the (001) plane to form dislocations and in twin orientation on the (112) plane to form faulty and twinned crystals. The anatase phase (denoted as $a$ ) was also derived from the dense fluorite-type (denoted as $f$ ) condensates by a martensitic-type transformation to form twin variants following the crystallographic relationship ${⟨001⟩}_f\Vert {\left[100\right]}_a$ with a habit plane close to ${\left\{100\right\}}_f\Vert {\left(001\right)}_a$ for a beneficial lower interfacial strain energy. The $f\text{‐}a$ transformation route of $\mathrm{Ti}{\mathrm{O}}_2$ can be rationalized by a metastable phase change in terms of the intersection of the internal energy versus cell volume curves of the polymorphs.