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首页> 外文期刊>International Journal of Production Research >Two-stage data mining for flaw identification in ceramics manufacture
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Two-stage data mining for flaw identification in ceramics manufacture

机译：两阶段数据挖掘，用于陶瓷制造中的缺陷识别

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Advanced ceramics are commonly manufactured by sintering high-purity powders. The design of ceramic elements is governed by its fracture strength, which is greatly influenced by microstructural flaws. Three ceramic powder preparation methods for ceramics manufacturing are considered in this paper—uniaxial pressing followed by isostatic pressing, flocculated slip casting, and dispersed slip casting. Their effects on the growth and characteristics of microstructure flaws and damage on the ceramic surface are investigated using a two-stage data-mining approach. In the first stage, digital microstructural images are mined to characterize the flaws and surface damage. In the second stage, an extreme value probability distribution is fitted using the information from stage 1. The extreme value distribution estimates large flaws which are highly correlated with subsequent fractures. Results of the two-stage data mining show that ceramic production method significantly affects flaw characteristics that, in turn, determine the ceramics' fracture strength.
机译：高级陶瓷通常通过烧结高纯度粉末来制造。陶瓷元件的设计取决于其断裂强度，而断裂强度受微观结构缺陷的影响很大。本文考虑了三种用于陶瓷制造的陶瓷粉末制备方法：单轴压制，等静压制，絮凝粉浆浇铸和分散粉浆浇铸。使用两阶段的数据挖掘方法研究了它们对陶瓷结构的缺陷的生长和特性以及陶瓷表面损伤的影响。在第一阶段，开采数字显微结构图像以表征缺陷和表面损伤。在第二阶段，使用来自阶段1的信息拟合极值概率分布。极值分布估计与后续裂缝高度相关的大缺陷。两阶段数据挖掘的结果表明，陶瓷生产方法会显着影响缺陷特征，进而决定陶瓷的断裂强度。

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