Synthesis of Barium Titanate Compounds with Enhanced Dielectric Properties at Room Temperature
The addition of 22 mol% of different zirconia containing powders: cubic stabilized zirconia (8YSZ), tetragonal zirconia (TZ3Y), and pure zirconia to barium titanate (BaTiO3) was studied. X-ray diffraction analysis of the sintered compact made of these powder mixtures confirmed the presence of single phase peroviskate-like structure. The microstructure of the sintered samples showed different grain sizes, for different kinds of zirconia additives. The relative permittivity and the dielectric loss of the sintered pellets were measured at different temperatures and frequencies. The highest relative permittivity was obtained for samples having 22 mol% 8YSZ. The yttria addition to barium titanate zirconate had been discussed.
Dielectric investigations of vanadium modified barium zirconium titanate ceramics obtained from mixed oxide method
Abstract Vanadium modified barium zirconium titanate ceramics Ba(Zr 0.10 Ti 0.90 )O 3 :2V (BZT:2V) were prepared from the mixed oxide method. According to X-ray diffraction analysis, addition of vanadium leads to ceramics free of secondary phases. Electrical characteristics reveal a dielectric permittivity at around 15,000 with low dielectric loss with a remnant polarization ( P r ) of 8 μC/cm 2 at 2 kV/cm. From the obtained results, we assume that vanadium substitution in the BZT lattice affects dielectric characteristics due to the electron-relaxation-mode in which carriers (polarons, protons, and so on) are coupled with existing dielectric modes.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.