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Applying Principal Component Analysis to Isolating Overall Variability of On-line Measurement Process

机译:主成分分析在确定在线测量过程总体变异性中的应用

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Measurement plays a significant role in helping an organization improve quality. With the greater reliance on quantitative measurements in modern manufacturing industry, the requirements for measurement system have dramatically increased. In the analysis of measured data, the variation of measurement result is composed of not only product variation, but also measurement variation. In order to evaluate, optimize and monitor manufacturing process, it is necessary to discriminate product variation and measurement variation from the measured data. In this paper, the intraclass correlation criterion for evaluating measurement system is provided on the basis of the model of process. Then the principal component analysis is used to isolate product variation and measurement variation from the measured data. Finally, an example from a six sigma project is presented to exhibit the implementing program. The results show that the program provides a simple method for isolating product and measurement variation and finding the relative usefulness of the measurement system.
机译:度量在帮助组织提高质量方面起着重要作用。随着现代制造业越来越依赖于定量测量,对测量系统的要求急剧增加。在对测量数据进行分析时,测量结果的变化不仅包括产品变化,而且还包括测量变化。为了评估,优化和监视制造过程,有必要将产品变化和测量变化与测量数据区分开。本文在过程模型的基础上,提出了用于评价系统的类内相关准则。然后,将主成分分析用于从测量数据中分离出产品变化和测量变化。最后,给出了一个来自六个西格玛项目的示例,以展示实施程序。结果表明,该程序提供了一种简单的方法来隔离产品和测量变量,并找到测量系统的相对有用性。

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