Marcus A. Agustin

Color match of machinable lithium disilicate ceramics: Effects of foundation restoration

STATEMENT OF PROBLEM Metal or white opaque foundation restorations may negatively affect the color of machinable lithium disilicate (MLD) ceramic restorations. PURPOSE The purpose of this study was to evaluate the effects of ceramic thickness and foundation restoration materials on the color of MLD restorations. MATERIAL AND METHODS Forty-five ceramic slices in 3 thicknesses (1.0 mm, 1.5 mm, 2.0 mm; 15 slices in each group) were made from low-translucency (LT) shade A1 IPS e.max CAD blocks. Resin cement (Multilink yellow) of 100-μm cement thickness was bonded to 3 different foundation restoration materials: silver-palladium (Ag-Pd) (Albacast) alloy, Type III gold (Midas), and white opaque foundation resin (Paracore white) to make the cement-foundation blocks. After optically connecting each ceramic specimen to the cement-foundation block, the color of each laminated combination was measured with a portable spectrophotometer (Vita EasyShade Compact). The color differences (ΔE) between the specimen assemblies and a control target block (a 12×14×14-mm crystalized shade A1 LT e.max CAD block) were calculated. Two-way ANOVA and general linear model were used to assess the effects of ceramic thickness, foundation materials, and their interactions to the resultant ΔE (α=.05). Clinical significance was determined by comparing color differences to perceptibility and acceptability thresholds by using the t test (α=.05). RESULTS Both ceramic thickness and foundation materials significantly affected the mean values of color difference (ΔE) of MLD restorations (P<.001). The mean value of ΔE decreased as the ceramic thickness increased. At a ceramic thickness of 1 mm, the color difference was above the clinically perceptible level (ΔE>2.6) with the 3 tested foundation materials (P<.001). As for the foundation materials, the ΔE was the lowest for Type III gold alloy, followed by Ag-Pd, then white opaque foundation resin. The color differences for Type III gold and a ceramic thickness of 1.5 or 2.0 mm were below the clinically perceptible level (ΔE<2.6) (P<.001). For Ag-Pd alloy or white opaque foundation resin, the color differences were above the clinically perceptible level (ΔE>2.6) (P<.001). Ag-Pd alloy reduced, the values of L* and b* parameters of MLD complexes, whereas the white opaque resin increased them. CONCLUSIONS Based on the results of the study, the colors of MLD ceramic restorations were affected by both the ceramic thickness and foundation restoration materials. Increasing ceramic thickness improved the resultant shade matching. Ag-Pd alloy made the ceramic restorations darker and bluish, whereas white opaque foundation resin made restorations brighter and yellowish.

A DYNAMIC COMPETING RISKS MODEL

We consider a series system with p components where the failure rate of each component depends on the residual number of defects present. Successive tasks are given to the system with each task completion time independent of each component failure time. Based on the outcomes over a fixed testing period, the asymptotic properties of the estimators of the component parameters, task completion parameters, and eventual system reliability are obtained.

Asymptotic and finite-sample properties of a reliability estimator for a competing risks system

The development of a series system where the failure mechanism of each component depends on an unknown constant rate and an unknown quantity representing the number of remaining defects in the component is considered. Successive tasks are assigned to the system and the corresponding task completion times are independent of the components’ failure times. Testing under two schemes, namely, a fixed time scenario and a u two-stage procedure is considered and the resulting asymptotic properties of the reliability estimator at test termination are presented. Moreover, the finite-sample properties of the resulting estimators under the two–stage testing scheme are investigated via Monte Carlo simulations.

Analysis of Recurrent Events from Repairable Systems

This article provides a review of recently developed dynamic stochastic models for recurrent events, in particular, recurrent events arising from monitoring of repairable systems. Inference methods such as the estimation of model parameters and goodness-of-fit testing are also discussed. The procedures described are demonstrated using a reliability data from load–haul–dump machines. Keywords: counting processes; dynamic models; goodness-of-fit testing; hazard models; maximum-likelihood estimation; partial likelihood; repairable systems

Parallel-Series and Series-Parallel Systems

This article will consider the reliability of two different systems, namely, a parallel-series system and a series-parallel system. The main focus is on finding the system reliability when the failure of each component depends on the length of time that the component is observed. In particular, the main result will be illustrated for exponentially distributed failure times. Keywords: coherent system; hazard function

Parallel, Series, and Series–Parallel Systems

This article will consider the system reliability of three different systems: series, parallel, and series–parallel systems. Interest is in finding the system reliability when the failure of each component depends on the length of time that the component is observed. Exponentially distributed failure times as well as Weibull failure times will be considered. Keywords: coherent system; competing risks; hazard function; redundancy; system reliability

A Stopping Criterion for a Dynamic Competing Risks Model

We consider the development of a competing risks system following a two-stage stopping rule. This stopping procedure takes into account a desired probability of successfully completing an assigned task. Upon termination, the asymptotic properties of the estimator of the system reliability, as well as the stopping time variable, are examined. Moreover, the asymptotic risk associated with the two-stage procedure is presented. This risk is anchored on a loss function that considers losses incurred in not attaining the desired reliability and that of excessive testing.