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Functional Weibull-based models of steel fracture toughness for structural risk analysis: estimation and selection

机译：基于Weibull的功能性钢断裂韧性模型，用于结构风险分析：估计和选择

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摘要

A key input component of numerous reliability studies of industrial components or structures, steel fracture toughness is usually considered as a random process because of its natural variability. Moreover, toughness presents a high sensitivity to temperature which also plays a fundamental role, as an environmental forcing, in such studies. Therefore a particular attention has to be paid to the assessment of its stochastic functional modelling, conducted by a statistical analysis of indirect measures. While a Weibull shape arising from statistical physics is recognized as one of the most relevant approach to represent local variability, the selection of functional parameters requires an accurate methodology of fracture toughness modelling. This article provides such a methodology, that solves inconsistencies in former data treatments. The innovation consists in three improvements: (a) the thickness correction of the steel specimen is included throughout the calculation and not performed a priori; (b) nonstandard but informative data are included in the assessment as censored data; (c) a chi-square test is developed to assess the model quality relatively to fracture toughness data, indexed by temperature. Illustrated by the exploration of a database feed by several European manufacturers, this complete methodology is implemented in a dedicated software tool.
机译：作为工业零件或结构的众多可靠性研究的重要输入要素，钢的断裂韧性由于其自然可变性，通常被认为是随机过程。此外，韧性表现出对温度的高度敏感性，在这种研究中，韧性也起着重要的环境作用。因此，必须对间接功能的统计分析进行特别评估，以评估其随机功能模型。虽然统计物理学产生的威布尔形状被认为是代表局部变化性的最相关方法之一，但功能参数的选择需要断裂韧性建模的准确方法。本文提供了一种方法，可以解决以前的数据处理中的不一致问题。该创新包括三个方面的改进：（a）在整个计算过程中都包括了对钢试样厚度的校正，而不是事先进行的； （b）评估中包括非标准但信息量大的数据作为审查数据； （c）进行卡方检验，以相对于以温度为指标的断裂韧性数据评估模型质量。几个欧洲制造商对数据库提要的探索说明了这一完整的方法，该方法是在专用的软件工具中实现的。

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