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It has been previously suggested that the fatigue lifetime of superelastic Ni–Ti might be improved if the R-phase were the parent to martensite rather than austenite. This body of work tests that hypothesis in two separate side-by-side fatigue tests both carefully constructed to match the superelastic properties in the two study arms. Both experiments show the R-phase parent to be more durable than the more commonly considered austenitic parent phase. The first experiment considers straight wire specimens fabricated from standard purity material, in a tension–tension fatigue test to 107 cycles, at mean strain ranging of 0.5–5.8% and strain amplitudes of 0.15–0.45%. The second experiment considers formed wire specimens in bending fatigue, more representative of realistic medical components, with a maximum mean strain of 1.2%, and maximum strain amplitudes ranging from 0.72 to 1.64%. Compared with the austenitic parent material, the R-phase material tolerated 0.1–0.3% higher strain amplitudes.

Tailoring the austenitic finish temperature (Af) for implantable medical devices is a common practice used in selecting heat treat parameters for Nitinol components. In addition to predicting a superelastic response under in-vivo conditions, Af is often correlated to mechanical properties such as tensile behavior and fatigue performance. Measuring the Af of Nitinol is seemingly simple, and therefore, a convenient tool to monitor process fluctuations. However, the reliance on Af can be highly misleading, resulting in undesirable mechanical properties of the Nitinol component. Experimental data presented here demonstrates how a single lot of superelastic Nitinol wire can be heat treated to achieve similar Af temperatures, but exhibiting large variations in tensile responses. Other measures of transformation temperatures, such as Ms and $${\text{M}}_{{\text{s}}}^{*}$$, are better indicators, but still not completely successful. It is thus concluded that one should, whenever possible, rely on the direct measurement of tensile properties at body temperature in order to design and validate heat treatment processes, and control those processes once designed.

This study investigates the thermal and mechanical properties that arise from aging Ni-rich Ni–Ti (Nitinol) at temperatures below 250 °C, well below those commonly used to fabricate medical devices. We demonstrate that the Ni50.8–Ti49.2 composition decomposes at temperatures as low as room temperature and discuss the unusual changes in thermal and mechanical behaviors compared to common aging treatments, such as separation of Martensite and R-phase transformations. Using such aging treatments, superelasticity can be achieved without the presence of austenite at body temperature (Af > 45 °C, well above the body temperature). Furthermore, the influence of R-phase on mechanical response and the disparity between thermal and mechanical behavior is discussed in detail.

A Ni50.3Ti41.7Hf8 alloy was studied after two-step aging treatments consisting of 300 °C for 12 h followed by 550 °C for different times. An anomalous change in transformation temperatures was observed as the second aging time was increased from 7.5 to 13.5 h. Initially with increased aging time (0.5–7.5 h) at 550 °C, coherency strain fields about H-phase precipitates increased. The corresponding backstress favored martensite formation, hence an increase in transformation temperatures. However, a point was eventually reached where misfit dislocations relaxed those strain fields and the effect was reduced, resulting in a decrease in transformation temperatures.

Transmission electron microscopy and mechanical testing were used to determine the effect of pre-aging (300 °C for 12 h) on microstructure and mechanical behavior of a series of Ni50.3Ti49.7 − xHfx shape memory alloys (x = 6, 8, 8.5, 9 at.%) prior to normal aging at 550 °C for 3.5 h. Pre-aging was found to promote homogenous nucleation of nanosized H-phase precipitates, resulting in improved mechanical stability and strength and 4% recoverable compression strain without permanent deformation. The absence of pre-aging generally resulted in heterogeneous formations of larger H-phase precipitates, primarily clustered along grain boundaries, and correlated with poorer mechanical behavior.

We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding an initial design bias. Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship annotations involving 57 classes. For object detection in particular, we provide 15x more bounding boxes than the next largest datasets (15.4M boxes on 1.9M images). The images often show complex scenes with several objects (8 annotated objects per image on average). We annotated visual relationships between them, which support visual relationship detection, an emerging task that requires structured reasoning. We provide in-depth comprehensive statistics about the dataset, we validate the quality of the annotations, we study how the performance of several modern models evolves with increasing amounts of training data, and we demonstrate two applications made possible by having unified annotations of multiple types coexisting in the same images. We hope that the scale, quality, and variety of Open Images V4 will foster further research and innovation even beyond the areas of image classification, object detection, and visual relationship detection., Accepted to International Journal of Computer Vision, 2020

In Reference 20, Alan Pelton was inadvertently omitted as an author. The title of the presentation has also been revised to match the conference program.

Deformation heterogeneities at the microstructural length-scale developed in polycrystalline shape memory alloys (SMAs) during superelastic loading are studied using both experiments and simulations. In situ X-ray diffraction, specifically the far-field high energy diffraction microscopy (ff-HEDM) technique, was used to non-destructively measure the grain-averaged statistics of position, crystal orientation, elastic strain tensor, and volume for hundreds of austenite grains in a superelastically loaded nickel-titanium (NiTi) SMA. These experimental data were also used to create a synthetic microstructure within a finite element model. The development of intragranular stresses were then simulated during tensile loading of the model using anisotropic elasticity. Driving forces for phase transformation and slip were calculated from these stresses. The grain-average responses of individual austenite crystals examined before and after multiple stress-induced transformation events showed that grains in the specimen interior carry more axial stress than the surface grains as the superelastic response “shakes down”. Examination of the heterogeneity within individual grains showed that regions near grain boundaries exhibit larger stress variation compared to the grain interiors. This intragranular heterogeneity is more strongly driven by the constraints of neighboring grains than the initial stress state and orientation of the individual grains.

Most deep architectures for image classification--even those that are trained to classify a large number of diverse categories--learn shared image representations with a single model. Intuitively, however, categories that are more similar should share more information than those that are very different. While hierarchical deep networks address this problem by learning separate features for subsets of related categories, current implementations require simplified models using fixed architectures specified via heuristic clustering methods. Instead, we propose Blockout, a method for regularization and model selection that simultaneously learns both the model architecture and parameters. A generalization of Dropout, our approach gives a novel parametrization of hierarchical architectures that allows for structure learning via back-propagation. To demonstrate its utility, we evaluate Blockout on the CIFAR and ImageNet datasets, demonstrating improved classification accuracy, better regularization performance, faster training, and the clear emergence of hierarchical network structures.

This article focuses on the specific aspects of nitinol that are of interest to medical device designers. It describes the physical metallurgy, physical properties, and tensile properties of the nitinol. The article discusses the factors influencing superelastic shape memory effects, fatigue, and corrosion in medical device design. It reviews the biocompatibility of nitinol based on corrosion behavior. The article explains the general principles, potential pitfalls, and key properties for manufacturing, heat treatment, and processing of nitinol.