Effect of Substrate Roughness on Adhesion and Structural Properties of Ti-Ni Shape Memory Alloy Thin Film

Ti-Ni shape memory alloy (SMA) thin films are very attractive material for industrial and medical applications such as micro-actuator, micro-sensors, and stents for blood vessels. An important property besides shape memory effect in the application of SMA thin films is the adhesion between the film and the substrate. When using thin films as micro-actuators or micro-sensors in MEMS, the film must be strongly adhered to the substrate. On the other hand, when using SMA thin films in medical devices such as stents, the deposited alloy thin film must be easily separable from the substrate for efficient processing. In this study, we investigated the effect of substrate roughness on the adhesion of Ti-Ni SMA thin films, as well as the structural properties and phase-transformation behavior of the fabricated films. Ti-Ni SMA thin films were deposited onto etched glass substrates with magnetron sputtering. Radio frequency plasma was used for etching the substrate. The adhesion properties were investigated through progressive scratch test. Structural properties of the films were determined via Feld emission scanning electron microscopy, X-ray diffraction measurements (XRD) and Energy-dispersive X-ray spectroscopy analysis. Phase transformation behaviors were observed with differential scanning calorimetry and low temperature-XRD. Ti-Ni SMA thin film deposited onto rough substrate provides higher adhesive strength than smooth substrate. However the roughness of the substrate has no influence on the growth and crystallization of the Ti-Ni SMA thin films.1

A Study on Deep Learning Application of Vibration Data and Visualization of Defects for Predictive Maintenance of Gravity Acceleration Equipment

Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hypergravity accelerator. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accelerometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The method proposed in this paper was trained with transfer learning, a deep learning model that replaced the VGG19 model with a Fully Connected Layer (FCL) and Global Average Pooling (GAP) by converting the vibration signal into a short-time Fourier transform (STFT) or Mel-Frequency Cepstral Coefficients (MFCC) spectrogram and converting the input into a 2D image. As a result, the model proposed in this paper has seven times decreased trainable parameters of VGG19, and it is possible to quantify the severity while looking at the defect areas that cannot be seen with 1D

Phase Stability and Properties of Ti-Nb-Zr Thin Films and Their Dependence on Zr Addition

Ternary Ti-Nb-Zr alloys were prepared by a magnetron sputtering method with porous structures observed in some of them. In bulk, in order to control the porous structure, a space holder (NH4HCO3) is used in the sintering method. However, in the present work, we show that the porous structure is also dependent on alloy composition. The results from Young’s modulus tests confirm that these alloys obey d-electrons alloy theory. However, the Young’s modulus of ternary thin films (≈80–95 GPa) is lower than that for binary alloys (≈108–123 GPa). The depth recovery ratio of ternary Ti-Nb-Zr thin films is also higher than that for binary β-Ti-(25.9–34.2)Nb thin film alloys