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Statistical estimation and testing for variation root-cause identification of multistage manufacturing Processes

机译:统计估计和多级制造过程变异根源识别的测试

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摘要

Root-cause identification for quality-related problems is a key issue in quality and productivity improvement for a manufacturing process. Unfortunately, root-cause identification is also a very challenging engineering problem, particularly for a multistage manufacturing process. In this paper, root-cause identification is formulated as a problem of estimation and hypothesis testing of a general linear mixed model. First, a linear mixed fault-quality model is built to describe the cause-effect relationship between the process faults and product quality. Then, the estimation algorithms developed for a general linear mixed model are adapted to estimate the process mean and variance. Finally, a hypothesis testing method is developed to determine if process faults exist in terms of statistical significance. A detailed experimental study illustrated the effectiveness of the proposed methodology.Note to Practitioners-Economic globalization brings intense competition among manufacturing enterprises. The key to succeed in this competitive climate is to rapidly respond to fast-changing market demands with high-quality and competitively priced products. To achieve this, we need to quickly identify root causes of quality-related problems in a complicated manufacturing system. However, the current widely adopted quality-control techniques focus more on monitoring than on root-cause identification. These techniques can efficiently detect the changes in the process but the root cause identification is often left to the plant engineers or operators. In this paper, a systematic estimation and testing method is proposed to identify the variational root causes in multistage manufacturing processes. First, a linear model is built based on the design information to describe the cause-effect relationship between the process faults and product quality. Then, an algorithm is developed to estimate the mean and variance of the process faults from the quality measurements of products. Finally, a statistical testing method is developed to determine if process faults (i.e. root causes) exist in terms of statistical significance. A detailed experimental study illustrates the effectiveness of this method. The method presented in this paper is a new quality-control technique and ca- n be used for quality improvement of multistage manufacturing processes.
机译:识别与质量相关的问题的根本原因是提高制造过程的质量和生产率的关键问题。不幸的是,根本原因识别也是一个非常具有挑战性的工程问题,尤其是对于多阶段制造过程而言。本文将根本原因识别公式化为一般线性混合模型的估计和假设检验问题。首先,建立线性混合故障质量模型,以描述过程故障与产品质量之间的因果关系。然后,为一般线性混合模型开发的估计算法适用于估计过程均值和方差。最后,开发了一种假设检验方法,以根据统计意义确定过程故障是否存在。详细的实验研究证明了该方法的有效性。从业者说明-经济全球化带来了制造企业之间的激烈竞争。在这种竞争激烈的环境中取得成功的关键是用高质量和价格具有竞争力的产品快速响应瞬息万变的市场需求。为此,我们需要在复杂的制造系统中快速找出与质量相关的问题的根本原因。但是,当前广泛采用的质量控制技术更多地集中于监视而不是根本原因识别。这些技术可以有效地检测过程中的变化,但是根本原因的识别通常留给工厂工程师或操作员。本文提出了一种系统的估计和测试方法,以识别多阶段制造过程中的变化根本原因。首先,基于设计信息建立线性模型,以描述过程故障与产品质量之间的因果关系。然后,开发了一种算法,可以根据产品的质量测量结果来估计过程故障的平均值和方差。最后,开发了一种统计测试方法来确定过程故障(即根本原因)是否存在统计学意义。详尽的实验研究证明了该方法的有效性。本文介绍的方法是一种新的质量控制技术,可用于多阶段制造过程的质量改进。

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