Comparative flow characteristics of selected dental materials with emphasis on thixotropic behavior.
Abstract Flow behavior of selected impression materials, dental cement liquids and fluoride gels were measured using a unique cup and bod viscometer. Programmable functions of the instrument allowed shear stress measurement during increasing and decreasing shear rates. Shear-dependent and time-dependent properties of plastic, pseudoplastic and thixotropic behavior were evaluated for these materials. The thixotropic index was also used to compare these materials. The impression materials tested showed pseudoplastic and thixotropic behavior, while the dental cements showed very little pseudoplastic or thixotropic behavior. The fluoride gels showed essentially plastic yield with minimal thixotropy.
Characterization of the Setting Process in Alginate Impression Materials
The functioning of an alginate impression material involves a change in its physical state from a viscous fluid to an elastic solid. (The paste obtained when water and the alginate powder are first mixed is a viscous fluid, since it flows irreversibly under the action of a force.) This change is effected by the reaction of calcium sulphate with the sodium or potassium alginate, thus forming a cross-linked molecular network.' The purpose of the present study has been to characterize the setting process in terms of easily measured physical parameters, and hence to make a rational study of the effect of those experimental variables that are of importance clinically.
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.