Q控制图灵敏性分析

对过程均值未知,过程标准偏差已知情况下的单值Q控制图与均值Q控制图检测过程均值一次偏移的灵敏性进行了分析.结果表明:在过程初始阶段,单值Q图的灵敏性要高于均值Q控制图.在过程初始阶段,建立单值Q控制图监测过程波动,累积一定数量的样本后,建立均值Q控制图监视过程波动.两种Q控制图互为补充,共同用于监视过程波动更有效.     

面向多品种小批量生产过程的Bootstrap控制图

针对多品种小批量生产过程中传统统计过程控制方法的局限性,介绍了Bootstrap控制图的概念,并对Boot-strap控制图的构造方法及上下控制限的确定方法进行了研究,针对计量值数据给出Bootstrap控制图的实施流程及判异规则,通过实证分析验证了在多品种小批量生产环境下Bootstrap控制图的有效性.     

VSI Q控制图应用研究

VSI Q控制图可以解决经典休哈特控制图在样本数据缺乏的情况下不能应用的问题,并且可以加快检出速度.具体方法是将样本数据转换成服从标准正态分布的统计数据,再利用可变抽样区间技术确定抽样间隔时间.应用研究证明,VSI Q控制图在缺乏样本数据时也能用于监控生产过程,快速检测出异常.     

控制图自动分析系统

描述了一种控制图自动分析系统,该系统具有自动计算各统计量与控制界限、绘图以及根据国标GB/T4091-2001对控制图进行自动分析等功能.通过该系统的应用实例,证明其具有很强的实用性.     

一种保证预设质量和过程稳态的SPC方法

提出三种过程质量指数(PQI)的过程质量指数系统,基于过程质量指数的统计公差提供了一个过程质量要求和控制图设计之间的标准化界面.通过基于过程质量指数的统计公差带增加对x--R或x--s控制图中线的约束,建立一种保证预设质量和过程稳态的统计过程控制新方法.这不仅增强了控制图的功能,也为过程质量规划、统计公差和保证预设质量的SPC相关参数的并行设计提供了指导.     

统计技术对质量过程控制数据的优化控制

针对统计过程控制技术在企业实施过程中难以真正实现质量控制功能的现状,对制造质量控制系统中数据的管理方法进行研究,提出依据产品结构配置的数据管理方法,解决制造质量数据分散、无序和不完整等问题,实现质量数据的合理存储和读取,大大提高了统计过程控制的准确性和可靠性。     

面向质量目标的零件批统计质量指标的分析与研究

为了有效解决多种质量指标与统计公差、质量控制统计参数间的并行设计问题,以统计公差技术理论为基础,应用统计过程控制技术研究并建立面向质量目标的零件批统计质量指标,通过计算机仿真及实测对统计质量指标进行了试验验证和分析,结果表明了统计质量指标建立的理论方法正确性及应用的可行性。     

基于多目标模型的MEWMA控制图的经济性统计性设计

MEWMA控制图具有能够检测具有多种质量特征的过程中的微小变化等优点,促使用户将该控制图应用于过程监控。在大多数MEWMA控制图的经济性统计性研究中,统计特性仅被认为是模型中数学规划的约束条件。在本文中,根据约束中成本函数对统计特性的依赖性,将统计特性也作为模型的目标函数之一,提出了MEWMA控制图的多目标经济性统计性设计模型。为了证明所提出的方法应用并评估其性能,基于多目标萤火虫算法对模型进行求解,并且与经济性模型进行比较,证明了多目标模型具有更好的效果。     

面向小批量生产的统计过程控制的研究

指出了在小批量生产环境下实施统计过程控制存在的问题,用概率积分变换理论,给出了控制过程均值、过程方差的标准化控制图,适用于小批量生产环境下对过程均值、过程方差的有效控制。     

Q控制图初始性能的改进

针对单值Q控制图在过程初始阶段所面临的突出问题,即检出力不强的弱点,提出一种改进单值Q控制图初始性能的质量控制图。它是建立在贝叶斯预测理论的基础上,应用在过程方差是未知的稳态生产过程的条件下。对控制图处于稳态和过程均值发生偏移时的性能及基于贝叶斯模型先验估计值对控制图的影响进行了讨论,结果表明控制图的特性适用于监视稳定生产过程的初始阶段,而这种特性恰好弥补了Q控制图在初始阶段性能不佳的弱点。     

Effective application of Q(R) charts in low‐volume manufacturing

Q charts provide means for statistical process control in low‐volume processes and start‐up phases of production. Concerns on their performance have led to research into different types of enhancements and much discussion on the appropriateness of these. Driven by the aim to implement control charts in the low‐volume production of advanced wafer steppers, we investigate the performance of additional run rules and tightening control limits on the traditional Q chart compared with an exponentially weighted moving average (EWMA). Furthermore, we develop an alternative QR chart based on the mean moving range as estimator of the process standard deviation and consider the economics of low‐volume processes by means of a specific cost model. The comparisons are based on the run length distributions after a permanent shift and trend, both with an onset early in the process. Real life examples are given for various important variables in wafer stepper production. It is concluded that the EWMA based on QR statistics provides the best performance throughout. Competing alternatives with almost equal performance are the EWMA of Q statistics and the combination of four tests of special causes (1‐of‐1, 2‐of‐3, 4‐of‐5 and 8‐of‐8) applied on either the Q or QR chart. Overall, the mean moving range performs better. Copyright © 1999 John Wiley & Sons, Ltd.     

CUSUM charts for detecting special causes in integrated process control

This paper investigates control charts for detecting special causes in an ARIMA(0, 1, 1) process that is being adjusted automatically after each observation using a minimum mean‐squared error adjustment policy. It is assumed that the adjustment mechanism is designed to compensate for the inherent variation due to the ARIMA(0, 1, 1) process, but it is desirable to detect and eliminate special causes that occur occasionally and produce additional process variation. It is assumed that these special causes can change the process mean, the process variance, the moving average parameter, or the effect of the adjustment mechanism. Expressions are derived for the process deviation from target for all of these process parameter changes. Numerical results are presented for sustained shifts, transient shifts, and sustained drifts in the process parameters. The objective is to find control charts or combinations of control charts that will be effective for detecting special causes that result in any of these types of parameter changes in any or all of the parameters. CUSUM charts designed for detecting specific parameter changes are considered. It is shown that combinations of CUSUM charts that include a CUSUM chart designed to detect mean shifts and a CUSUM chart of squared deviations from target give good overall performance in detecting a wide range of process changes. Copyright © 2009 John Wiley & Sons, Ltd.     

Attribute control charts using generalized zero‐inflated Poisson distribution

This paper presents a control charting technique to monitor attribute data based on a generalized zero‐inflated Poisson (GZIP) distribution, which is an extension of ZIP distribution. GZIP distribution is very flexible in modeling complicated behaviors of the data. Both the technique of fitting the GZIP model and the technique of designing control charts to monitor the attribute data based on the estimated GZIP model are developed. Simulation studies and real industrial applications illustrate that the proposed GZIP control chart is very flexible and advantageous over many existing attribute control charts. Copyright © 2008 John Wiley & Sons, Ltd.     

Multivariate statistical process control charts: an overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart‐type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial least squares (PLS). Finally, we describe the most significant methods for the interpretation of an out‐of‐control signal. Copyright © 2006 John Wiley & Sons, Ltd.     

Process monitoring research with various estimator-based MEWMA control charts

Multivariate exponentially weighted moving average (MEWMA) control chart with five different estimators as population covariance matrix is rarely applied to monitor small fluctuations in the statistical process control. In this article, mathematical models of the five estimators (S₁, S₂, S₃, S₄, S₅) are established, with which the relevant MEWMA control charts are obtained, respectively. Thereafter, the process monitoring performance of the five control charts is simulated. And the simulation results show that the S₄ estimator-based MEWMA control chart is of the best performance both in step offset failure mode and ramp offset failure mode. Since the inline process monitoring of photovoltaic manufacturing is intended to be a problem of multivariate statistics process analysis, the feasibility and effectiveness of the proposed model are elaborated in the case study during the cell testing and sorting process control for the fabrication of multicrystalline silicon solar cells.     

A comparison study on effectiveness and robustness of control charts for monitoring process mean and variance

This article compares the effectiveness and robustness of nine typical control charts for monitoring both process mean and variance, including the most effective optimal and adaptive sequential probability ratio test (SPRT) charts. The nine charts are categorized into three types (the type, CUSUM type and SPRT type) and three versions (the basic version, optimal version and adaptive version). While the charting parameters of the basic charts are determined by common wisdoms, the parameters of the optimal and adaptive charts are designed optimally in order to minimize an index average extra quadratic loss for the best overall performance. Moreover, the probability distributions of the mean shift δ_µ and standard deviation shift δ_σ are studied explicitly as the influential factors in a factorial experiment. The main findings obtained in this study include: (1) From an overall viewpoint, the SPRT‐type chart is more effective than the CUSUM‐type chart and type chart by 15 and 73%, respectively; (2) in general, the adaptive chart outperforms the optimal chart and basic chart by 16 and 97%, respectively; (3) the optimal CUSUM chart is the most effective fixed sample size and sampling interval chart and the optimal SPRT chart is the best choice among the adaptive charts; and (4) the optimal sample sizes of both the charts and the CUSUM charts are always equal to one. Furthermore, this article provides several design tables which contain the optimal parameter values and performance indices of 54 charts under different specifications. Copyright © 2011 John Wiley & Sons, Ltd.