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Monitoring imprecise fraction of nonconforming items using p control charts

机译:使用p控制图监控不合格品的不精确分数

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The quality characteristics, which are known as attributes, cannot be conveniently and numerically represented. Generally, the attribute data can be regarded as the fuzzy data, which are ubiquitous in the manufacturing process and cannot be measured precisely and often be collected by visual inspection. In this paper, we construct a p control chart for monitoring the fraction of nonconforming items in the process in which fuzzy sample data are collected from the manufacturing process. The resolution identity - a well-known theorem in the fuzzy set theory - is invoked to construct the control limits of fuzzy-p control charts using fuzzy data. In order to determine whether the plotted imprecise fraction of nonconforming items is within the fuzzy lower and upper control limits, we also propose a ranking method for a set of fuzzy numbers. Using the fuzzy-p control charts and the proposed acceptability function to classify the manufacturing process allows the decision-maker to make linguistic decisions such as rather in control or rather out of control. A practical example is provided to describe the applicability of the fuzzy set theory to a conventional p control chart.
机译:不能方便地用数字表示被称为属性的质量特征。通常,属性数据可以看作是模糊数据,它们在制造过程中无处不在,无法精确测量,并且经常通过目测来收集。在本文中,我们构造了一个p控制图,用于监控从制造过程中收集模糊样本数据的过程中不合格品的比例。分辨率身份-模糊集理论中的一个著名定理-被调用以使用模糊数据来构造Fuzzy-P控制图的控制极限。为了确定所绘制的不合格项的不精确分数是否在模糊的控制下限和控制上限之内,我们还针对一组模糊数提出了一种排序方法。使用Fuzzy-P控制图和建议的可接受性函数对制造过程进行分类,使决策者可以做出语言决策,例如控制或不控制。提供了一个实际示例来描述模糊集理论对常规p控制图的适用性。

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