scholarly journals Influence of Surface Treatment on Strength Distribution of Vita VMK 68 Dental Porcelains

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Serkan Nohut ◽  
Ahmet Tasdemir ◽  
Suleyman Aykut Korkmaz
Keyword(s):  
Distribution Function ◽  
Weibull Distribution ◽  
Statistical Model ◽  
Surface Treatment ◽  
Goodness Of Fit ◽  
Strength Distribution ◽  
Surface Treatments ◽  
Bending Test ◽  
Dental Ceramics ◽  
Weibull Distribution Function

Weibull distribution function is the most commonly used statistical model for the investigation of mechanical properties of dental ceramics and design process with dental ceramics. However, it is still unclear whether the Weibull distribution function is the most appropriate function for fitting the strength data of dental ceramics with different surface treatments. In this paper, three-point bending test results of feldspathic body porcelain (Vita VMK 68) specimens with four different surface treatments are analysed. According to goodness-of-fit tests (Anderson-Darling test, Kolmogorov-Smirnov test, and Akaike information criterion), it is shown that the type of surface treatment has an important influence on deviation of strength distribution from perfect Weibull statistics. It is concluded that estimation of the most suitable statistical model for Vita VMK 68 is not only a material-dependent but also a process-dependent (machining of the specimens) procedure.

Statistical Analysis of the ASME KIc Database

1998 ◽  
Vol 120 (1) ◽  
pp. 24-28 ◽  
Author(s):  
M. A. Sokolov
Keyword(s):  
Fracture Toughness ◽  
Distribution Function ◽  
Lower Bound ◽  
Weibull Distribution ◽  
Master Curve ◽  
Lower Boundary ◽  
Transition Range ◽  
Weibull Distribution Function ◽  
American Society ◽  
Function Models

The American Society of Mechanical Engineers (ASME) KIc curve is a function of test temperature (T) normalized to a reference nil-ductility temperature, RTNDT, namely, T – RTNDT. It was constructed as the lower boundary to the available KIc database. Being a lower bound to the unique but limited database, the ASME KIc curve concept does not discuss probability matters. However, a continuing evolution of fracture mechanics advances has led to employment of the Weibull distribution function to model the scatter of fracture toughness values in the transition range. The Weibull statistic/master curve approach was applied to analyze the current ASME KIc database. It is shown that the Weibull distribution function models the scatter in KIc data from different materials very well, while the temperature dependence is described by the master curve. Probabilistic-based tolerance-bound curves are suggested to describe lower-bound KIc values.

scholarly journals Results of the Verification of the Statistical Distribution Model of Microseismicity Emission Characteristics

2016 ◽  
Vol 61 (3) ◽  
pp. 489-496
Author(s):  
Aleksander Cianciara
Keyword(s):  
Distribution Function ◽  
Weibull Distribution ◽  
Goodness Of Fit ◽  
Cumulative Distribution Function ◽  
Statistical Distribution ◽  
Cumulative Distribution ◽  
Distribution Model ◽  
Emission Characteristics ◽  
Goodness Of Fit Test ◽  
Empirical Cumulative Distribution Function

Abstract The paper presents the results of research aimed at verifying the hypothesis that the Weibull distribution is an appropriate statistical distribution model of microseismicity emission characteristics, namely: energy of phenomena and inter-event time. It is understood that the emission under consideration is induced by the natural rock mass fracturing. Because the recorded emission contain noise, therefore, it is subjected to an appropriate filtering. The study has been conducted using the method of statistical verification of null hypothesis that the Weibull distribution fits the empirical cumulative distribution function. As the model describing the cumulative distribution function is given in an analytical form, its verification may be performed using the Kolmogorov-Smirnov goodness-of-fit test. Interpretations by means of probabilistic methods require specifying the correct model describing the statistical distribution of data. Because in these methods measurement data are not used directly, but their statistical distributions, e.g., in the method based on the hazard analysis, or in that that uses maximum value statistics.

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