Colour centres in plasma-sprayed hydroxyapatite.
Very pure hydroxyapatite is diamagnetic and shows no electron paramagnetic resonance (EPR) spectrum. Plasma-sprayed coatings made with such a pure hydroxyapatite contain paramagnetic point-defects. Irradiated deposits show a strong EPR absorption assigned to an O2- anion adjacent to a calcium vacancy, with anisotropic parameters, g1 = 2.004, g2 = 2.013 and g3 = 2.038. The EPR spectra and colour fade away following thermal annealing. These features are discussed in terms of point-defects in the hydroxyapatite crystal structure.
ESR on the hydroxyl ion vacancies in the apatites.
Heating biological apatites and synthetic apatites in the stream of inert gas saturated with heavy water vapor, introduces OD ions which substitute for the hydroxyl (OH) ions and partially for the OH vacancies, depending on the heating time and the gas flow rate. This has been verified by ESR and partially by IR. Deuteration gives no serious alterations of the a-axis dimension or of the PO4 bands in the IR spectra. These findings have been interpreted as suggesting that the OH ions diffuse out and the OD ions diffuse in through specific pathways, and the diffusing ions interact permanently with none of the other ions in the apatite structure. Heating in dry inert gas results in the formation of hydroxyl ion vacancies. The ESR spectra of these samples in the g = 2.06 region have been interpreted in terms of O-2 and O-. The shortening of the a-axis dimension in this case has been accounted for based on the loss of structurally incorporated H2O and the loss of the OH ions on the hexad axis.
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.