Spin Coated YSZ Thin Films on Silicon Substrate
Yttria Stabilized Zirconia (YSZ) thin films (<10 μm) were fabricated by spin coating technique on silicon (Si). Parameters such as concentration ratio of YSZ powder to solvent were varied accordingly. The effects of these variations were investigated and discussed. Using X-ray Diffraction and Raman Spectroscopy, the crystal structure of the samples were determined. The 8-YSZ thin films have cubic fluorite structure. The X- ray diffraction patterns were in agreement with the samples' Raman spectra. Pores were evident on single coated substrates but were minimized using higher YSZ concentration. Index Terms—Yttria Stabilized Zirconia,YSZ, spin-coating, thin film ——————————  ——————————
YSZ thin films deposited by e-beam technique
Abstract YSZ thin films were grown evaporating cubic and tetragonal phase ZrO 2 stabilized by 8 wt.% of Y 2 O 3 (8% of YSZ) ceramic powders by using e-beam deposition technique. Operating technical parameters that influence thin film properties were studied. The influence of substrate crystalline structure on growth of deposited YSZ thin film was analyzed there. The YSZ thin films (1.5–2 μm of thickness) were deposited on three different types of substrates: Al 2 O 3 , optical quartz (SiO 2 ), and Alloy 600 (Fe–Ni–Cr). The dependence of substrate temperature, electron gun power, and phase of ceramic powder on thin film structure and surface morphology was investigated by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The substrate temperature was changed in the range of 20–600° C (during the YSZ thin film deposition) and its influence on the crystallinity of deposited YSZ thin films was analyzed. It was found that electron gun power and substrate temperature has the influence on the crystallite size, and texture of YSZ thin films. Also, the substrate has no influence on the crystal orientation. The crystallite size varied between 20 and 40 nm and increased linearly changing the substrate temperature. The crystal phase of evaporated YSZ powder has the influence on the structure of the deposited YSZ thin films.
Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review
One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.