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Weibull parameter estimation and goodness-of-fit for glass strength data

机译:威布尔参数估计和玻璃强度数据的拟合优度

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Strength data from macroscopically identical glass specimens is commonly described by a two-parameter Weibull distribution, but there is lack of research on the methods used for fitting strength data to the Weibull distribution. This study investigates 4 different methods for fitting data and estimating the parameters of the Weibull distribution namely, good linear unbiased estimators, least squares regression, weighted least squares regression and maximum likelihood estimation. These methods are implemented on fracture surface strength data from 418 annealed soda-lime-silica glass specimens, grouped in 30 nominally identical series, including as-received, naturally aged and artificially aged specimens. The strength data are evaluated based on their goodness of fit. Comparison of conservativeness of strength estimates is also provided. It is found that a weighted least squares regression is the most effective fitting method for the analysis of small samples of glass strength data. (C) 2018 The Authors. Published by Elsevier Ltd.
机译:来自宏观上相同的玻璃样品的强度数据通常由两参数的威布尔分布来描述,但是缺乏用于将强度数据拟合到威布尔分布的方法的研究。这项研究调查了4种不同的拟合数据和估计Weibull分布参数的方法,即良好的线性无偏估计量,最小二乘回归,加权最小二乘回归和最大似然估计。这些方法是根据418个退火钠钙硅玻璃样品的断裂表面强度数据实施的,该样品分为30个名义上相同的系列,包括已接收的,自然时效和人工时效的样品。强度数据基于其拟合优度进行评估。还提供了强度估计值保守性的比较。发现加权最小二乘回归是分析玻璃强度数据小样本的最有效拟合方法。 (C)2018作者。由Elsevier Ltd.发布

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