Dimensional accuracy of stone casts made from silicone-based impression materials and three impression techniques.
The aim of this study was to measure and compare the dimensional accuracy of stone casts of a partially edentulous mandibular arch made by two condensation silicones, two addition silicones and three impression techniques (1-step putty/light-body, 2-step putty/light-body and monophase technique). A partially edentulous standard stainless steel mandibular arch cast was obtained with reference points on the permanent mandibular right and left canines, and permanent mandibular right and left second molars. The anteroposterior distances (between the mandibular left canine and second molar and between the mandibular right canine and second molar) and transverse distances (between the mandibular left and right canines and between the mandibular left and right second molars) of stainless steel cast were measured by a microscope at 30× magnification and 0.5 µm accuracy. All impressions were made with condensation or addition silicones. The 1- and 2-step putty/light-body impressions were accomplished with putty and light-body materials and the monophase impressions with light-body material only. After the impression procedures, accuracy of each material and technique was assessed measuring the stone casts poured (n=5) from the impressions, by the same microscope. The differences between the values of stone cast and stainless steel cast were calculated, presented as percentages and analyzed statistically by two-way ANOVA and the means compared by Tukey's test (α=0.05). All distances measured on stone casts showed altered dimensions with significantly negative linear changes (shrinkage) as compared to those of the stainless steel cast. The stone casts made from the addition silicones were dimensionally more accurate. No differences were found among the impression techniques.
DIMENSIONAL STABILITY OF VINYLSILOXANETHER IMPRESSION MATERIAL AFTER AUTOCLAVE STERILIZATION AN IN VITRO STUDY
Dental impressions are often neglected in clinical practice as a source of spread of infectious diseases. In Pakistan more than fifty percent of the clinicians do not disinfect impressions. Blood and saliva contaminated impressions should be either disinfected or sterilized before further handling. Aim of this study was to determine effect of autoclave sterilization on the time dependent dimensional stability of vinylsiloxanether impression material. Impressions of stainless steel die (ADA/ANSI specification No. 19), were made of Vinylsiloxanether. Out of 40 specimens, half were allotted to group A and were autoclaved at 134oC AND 20 psi for 5 minutes. Rest of the specimens (Group B) served as a control. Dimensional changes were calculated for two time intervals i.e. after one hour and after twenty-four hours. Samples were scanned and images were analyzed by using Adobe reader XI (AcroRd 32) software. Paired t-test showed significant difference (p = 0.007) between readings at first hour (-0.047%) and twenty fourth hour (0.084%) in group A. Whereas independent t-test showed no significant difference in group A and B for both time intervals i.e. at first hour (p = 0.162) and twenty fourth hour (p = 0.148). It was concluded that Vinylsiloxanether can be sterilized in an autoclave without any deleterious change in dimensions.
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.