简化的测量过程统计控制方法

休哈特控制图平均值控制界限的设置对于计量标准或测量设备来说有很大的局限性,根据计量标准或测量设备的特点,提出简化的测量过程统计控制方法,该方法省去了标准偏差或极差控制图。根据标准偏差试验结果的大小,提出了子组最少测量次数的确定方法,平均值控制界限以各组平均值算得的标准偏差来设置,平均值控制图异常的判断准则简化为3种判据,进而大大减少了实施统计控制的工作量,便于推广应用。     

测量过程统计控制

一、引言新标准在对测量过程控制的要求较老标准髙,这从标准的题目即可看出。在前文“测量过程设计”与控制中己经提到,控制测量过程的质量的方式有多种,如:盲样测试,不同人员、仪器、测量方法比对,分析一个物品不同特性结果的相关性等。但最完善的方式是测量过程统计控制。因此本文就此作一简介。二、为什么要对测量过程实行统计控制世界著名的质量管理专家戴明(W.E.Deming)说:“生活就是变异,世界上没有两件产品或其特性是完全相同的,因为生产它们的过程存在着许多变异。”在现代化的大生产中,为了控制这种变异,也就是控制产品质量,引入…     

测量过程统计控制

简要介绍了测量过程统计控制方法的历史背景和基本流程,详细介绍了核查标准、过程参数、测量过程统计控制的统计检验法（包括t-检验和F-检验）和控制图法（包括Shewhart控制图、EWMA控制图和标准偏差控制图）,以及测量过程失控的判定与处置方法,最后介绍了测量过程统计控制方法的优点和不足。     

测量过程统计控制

ISO10012：2003《测量管理体系测量过程和测量设备的要求》在“引言”(Introduction)开章明义地指出：测量管理体系的目标是管理由于测量设备和测量过程可能产生的不正确结果而影响该组织的产品质量的风险。用于测量管理体系的方法(Methods)包括从基本的测量设备验证到测量过程控制中统计技术的应用。     

二等铂电阻温度计标准装置测量过程统计控制的实践

由于二等铂电阻温度计标准装置其组成的计量设备较多,在开展量值传递时测量不确定度分量多,且受外界影响量影响较大,不易保证其测量品质.因此,在精选核查标准的前提下,用数理统计分析的方法,使测量过程均处于连续的统计控制之中,保证了测量结果的准确可靠.     

测量过程的统计控制

本文根据科技、经济和社会发展的需求，对美国率先开展的“计量保证方案”服务的核心问题－测量过程的统计，从基本概念，原理到实施条件，步骤以及检验方法等，进行了系统，简要的论述。     

面向半导体制造业的统计过程分析与控制

传统的统计过程控制方法不能完全适应半导体制造业生产形式需要。本文在分析半导体光电封装制造模式的特点和实施过程控制所面临的问题的基础上,提出一种基于聚类分析的统计质量控制方法。通过实证分析,证实了该方法的可操作性并取得了良好的实际效果。     

测量过程的统计控制——控制图

<正>JJF1033-2008《计量标准考核规范》中的主要计量特性之一是计量标准的稳定性,计量标准通常由计量标准器与配套设备组成。计量标准的稳定性是指计量标准保持其计量随时间恒定的能力;测量过程的统     

运用控制图 做好测量过程控制

0引言计量标准的重复性和稳定性是计量标准的两个主要计量特性,通常需要对计量标准的重复性和稳定性进行考核。但是对于已经有效建立测量过程统计控制的计量标准,可以不必再单独进行重复性试验和稳定性考核。同时JJF1033-2008《计量标准     

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.     

A mixed control chart to monitor the process

In this paper, we propose a mixed control chart to monitor the process quality using attribute data combined with variable data. The proposed control chart proceeds like an np control chart based on the number of nonconforming parts but requires variable data only when the decision is indeterminate. The control coefficients are determined by considering the in-control and the out-of-control average run lengths for various specified parameters. The extensive tables are provided for the industrial use. The advantages of the proposed control chart are discussed over the traditional np control chart.     

Application of X̄ control chart with modified limits in process control

The conventional Shewhart X̄ chart is developed based on the assumption that the within‐sample variation is due to the inherent process variation, and any significant variation between samples is attributed to the existence of assignable causes. In the manufacturing industry there are processes where there is variation between samples due to the inherent process variation. A straightforward application of the conventional Shewhart X̄ chart would thus result in more frequent false alarms. The problems associated with various Shewhart X̄ charts in monitoring such a process are discussed using a real data set from an integrated circuit (IC) assembly process. A Shewhart X̄ chart with modified limits is adapted for such a process. In addition to the usual ability to signal for assignable causes, the X̄ chart with modified limits is also developed as a tool to signal the need for adjustment of controllable process variables for improving the process capability. Practical application of this chart in monitoring an IC assembly process is discussed. Copyright © 1999 John Wiley & Sons, Ltd.     

Statistical monitoring and control of a low defect process

Advanced technologies today are such that it is possible to keep the occurrence of defects in manufactured products at very low levels. The use of the conventional c-chart for statistical control of defects in such products would encounter serious practical difficulties because the low defect counts would render invalid the theoretical assumptions used in the construction of the chart. Based on reasoning with fundamental probability distributions, this paper offers a simple and reliable solution that is particularly suited to on-line inspection and testing operations such as those found in an automated manufacturing environment.     

面向质量目标的Q控制图设计

针对大批量生产开始阶段的过程监控,提出了一种基于预定质量目标的Q控制图监控方法.其基本思路是利用面向质量目标的统计公差技术与Q控制图相结合应用,以实现大批量过程开始阶段均值和方差未知时面向质量目标的过程监控.基于质量目标建立统计公差(CP*,k*),并将该统计公差转化为基于给定置信概率的对CP和k的估计值的判定条件.通过案例分析,面向质量目标的Q控制图能够在过程保持受控状态的前提下以一定置信概率保证质量目标.     

Detection of process upsets—sample autocorrelation control chart and group autocorrelation control chart applications

This paper presents an application of the sample autocorrelation function to statistical process control where the process data are serially correlated. Two innovative control charts are illustrated: the sample autocorrelation control chart and the group autocorrelation control chart. The important feature is that these control charts will detect shifts in the autocorrelative structure as well as shifts in the mean of the process. The sample autocorrelation function is typically used to identify an appropriate ARIMA model for a time series. The sample autocorrelation function may also be used as the basis of control charts to detect process upsets. Two unique features distingush this application of the sample autocorrelation function to statistical process control. First, the sample autocorrelations are exponentially smoothed estimates. This allows the user to control the sensitivity of the sample autocorrelation control chart. Secondly, the sample autocorrelation control chart is applied to a continuous stream of data—rather than to a static set of data that has been used to fit an ARIMA model.     

Statistical process monitoring using an empirical Bayes multivariate process control chart

In this paper, we describe the theory underlying an empirical Bayesian approach to monitoring two or more process characteristics simultaneously. If the data is continuous and multivariate in nature, often the multivariate normal distribution can be used to model the process. Then, using Bayesian theory, we develop techniques to implement empirical Bayes process monitoring of the multivariable process. Lastly, an example is given to illustrate the use of our techniques. Copyright © 2001 John Wiley & Sons, Ltd.     

测量过程控制技术的工程应用研究

文章根据国际质量、测量管理体系发展的要求，通过对测量过程控制技术的深入探讨，阐述了测量过程控制分析结论对产品和过程可接受性决策的影响，并以实例说明测量过程控制分析技术在企业工程实际中的具体应用。     

Statistical Tests to Validate Predictive Models

This article presents validation tests to check a predictive model over time. These validation tests are like individuals, and S control charts. The individuals validation test is used when there are no replicates at the checked time points. The and S validation tests can be used when there are replicates. Their power is evaluated under various scenarios via a simulation study. Based on these results, the and S validation tests are recommended when there are replicate measurements. Copyright © 2014 John Wiley & Sons, Ltd.     

Statistical monitoring of nonlinear product and process quality profiles

In many quality control applications, use of a single (or several distinct) quality characteristic(s) is insufficient to characterize the quality of a produced item. In an increasing number of cases, a response curve (profile) is required. Such profiles can frequently be modeled using linear or nonlinear regression models. In recent research others have developed multivariate T² control charts and other methods for monitoring the coefficients in a simple linear regression model of a profile. However, little work has been done to address the monitoring of profiles that can be represented by a parametric nonlinear regression model. Here we extend the use of the T² control chart to monitor the coefficients resulting from a parametric nonlinear regression model fit to profile data. We give three general approaches to the formulation of the T² statistics and determination of the associated upper control limits for Phase I applications. We also consider the use of non‐parametric regression methods and the use of metrics to measure deviations from a baseline profile. These approaches are illustrated using the vertical board density profile data presented in Walker and Wright (Comparing curves using additive models. Journal of Quality Technology 2002; 34:118–129). Copyright © 2007 John Wiley & Sons, Ltd.