小批量生产条件下的统计过程控制研究

从小批量生产的定义入手，分析传统的休哈特控制图应用在小批量生产条件下存在的问题及局限性。对目前小批量生产质量控制的方法进行简要分析。在此基础上，提出一种适合于小批量生产质量控制的新思路，并用实际数据对提出的方法与传统的休哈特控制图方法进行了对比，说明这种新思路应用在小批量生产过程中的可行性和有效性。     

累积和控制图与传统控制图同时监控过程均值

质量管理中广泛使用的休哈特控制图，对异常原因引起的均值大的偏移比较有效，但对于小的持续的偏移却不敏感；累积和控制图对小的持续性偏移有效。但对瞬间偏移不敏感。在过程质量的监控中，同时采用这两种控制图来进行监控，包容了两种控制图的优点，弥补了彼此的不足。在一定条件下，提出了一种新的控制图——累积和和休哈特混合控制图，效果明显优于累积和控制图或休哈特控制图。     

竞争型人工神经网络的统计过程控制模式识别

统计过程控制(SPC)在现代制造和服务行业中是最常应用的性能改进工具之一,休哈特控制图作为SPC的基本方法,在质量管理领域得到了充分的应用,但是控制图的识别一直比较困难.为此,将神经网络引入到休哈特控制图中,用神经网络的方法对质量控制中控制图进行了识别,并建立了以竞争型人工神经网络为基础的新的神经网络,判断出控制图中的基本失效模式.     

多变异源过程控制与指数加权移动平均控制图的应用研究

改进休哈特控制图能够解决多变异源过程控制虚发警报错误过高的问题,但是对均值偏移的检出力过低。本文研究的目的是将指数加权移动平均(EWMA)控制图用于多变异源过程控制,提高控制图对均值偏移的检出力。通过对多个变异源的方差分析,基于对部分方差估计,给出指数加权移动平均控制图在多变异源过程控制的应用方法。通过计算指数加权移动平均控制图的平均运行长度,并与改进的休哈特控制图作比较,证明指数加权移动平均控制图在检测多变异源过程均值偏移时检出力有较大提高,应用实例的结果表明当偏移量小于1σ时,检出力能提高10倍以上。     

SPC技术在小批量多品种短期生产中的应用

本文分析了传统SPC技术在小批量多品种短期生产中质量控制所存在的问题,应用数理统计方法．得出一种基于正态过程．采取以数据标准化为基础的Z-MR图．可有效用于小批量多品种下对短期生产实施过程质量控制。     

面向多品种小批量生产的SPC研究

分析了传统控制图应用于多品种小批量生产环境下存在的缺陷,从而提出了一种新的面向多品种小批量生产的统计过程质量控制图--动态控制限控制图(X-R图),它采用动态的控制界限方法建立控制图,极大地避免了"虚发警报"的发生.并结合实例验证了该控制图的有效性.     

累积和控制图在摩托车车灯闪光器生产过程中的应用

摩托车闪光器的闪烁频率检测往往使用基于3σ原理的休哈特(Shewhart)控制图,但是经典的休哈特控制图对频率发生的小漂移不够敏感。当小漂移发生时,会导致闪光器的闪烁频率调整不及时,采用累积和控制图(CUSUM控制图)来监控闪光器的闪烁频率,能及时监控闪光器的闪烁频率发生的中小漂移,提高了控制图的效率,在保证产品质量的同时也降低了生产成本。     

液化气自动灌装称重测量系统统计控制技术

本文主要阐述了公司在建立和实施ISO9000质量管理体系的过程中．利用休哈特控制图原理对液化气自动灌装线称重测量过程进行控制，使设备等因素处于受控状态，减少、消除不合格品，预防不合格品的产生。     

基于小批量生产的统计质量控制

以多品种小批量为主的高智能、高柔性自动化生产加工系统是未来的主导生产方式。传统的控制图应用于小批量生产质量控制时，存在着“误发警报”概率增加的问题。新的基于小样本的控制图模型，其控制界限可靠稳定，不受样本组数量的限制，较好地解决了小批量产品生产质量控制中频繁出现错误告警现象的问题。     

面向先进制造系统的小批量统计质量控制的研究

分析了传统控制图应用于先进制造系统面临的问题，提出一种新的基于小批量生产环境的质量控制图，它采用了可变的控制界限，不管样本数量多少，都能使误发警报概率保持较小的定值，从而避免了错误的频繁报警，无谓的停机检查和多次调整，提高了控制图对生产工序的监控效率。     

小批量过程多变异控制图技术研究

针对小批量过程的主要控制图技术中目标图、比例图和标准变换图,归纳出小批量过程控制的同分布原理。考虑到不同小批量之间的差异,以目标均值控制图为例,对控制图进行改进,给出多变异目标均值控制图的建立步骤。通过一个例子发现,该方法既实现了小批量过程多变异的控制,又降低了控制图虚发警报的概率。     

An approach to controlling two dependent process steps with autocorrelated observations

The observations from the process output are always assumed independent when using a control chart to monitor a process. However, for many processes the process observations are autocorrelated. This autocorrelation can have a significant effect on the performance of the control chart. This paper considers the problem of monitoring the mean of a quality characteristic X on the first process step and the mean of a quality characteristic Y on the second process step, in which the observations X can be modeled as an AR(1) model and observations Y can be modeled as a transfer function of X since the state of the second process step is dependent on the state of the first process step. To effectively distinguish and maintain the state of the two dependent process steps, the Shewhart control chart of residual and the cause-selecting control chart are proposed. The proposed control charts’ performance is measured by the rate of alarm on the proposed charts. From numerical analysis, it shows that the performance of the proposed control charts is much better than the misused Hotelling T² control chart and the individual Shewhart X and Y control charts.     

The performance of the Shewhart sign control chart for finite horizon processes

In many manufacturing environments, the production horizon of the same part code between two consecutive set-ups should be limited to a few hours or shifts. When 100 % sampling is not possible, on-line quality control on a quality characteristic should be immediately started by means of a control chart. In this paper, we investigate the statistical performance of a nonparametric (distribution-free) Shewhart Sign (SN) control chart for monitoring the location of a quality characteristic in a production process with a finite horizon and a small number of scheduled inspections. The observations taken from the process are assumed to be continuous random variables. By implementing a SN control chart, any model assumption about the distribution of observations is needless to guarantee a nominal in-control (IC) performance: after each process set-up, this overcomes the important problem of lack of information about the distribution of the observations collected for the quality characteristic to be monitored. An extensive simulation study is conducted to compare the statistical performance of the distribution-free Shewhart SN control chart to the normal theory-based Shewhart Student’s t control chart: several types of distributions of observations and different numbers of scheduled inspections are considered to show the advantages related to the implementation of the Shewhart SN control chart. An illustrative example presents the implementation of the Shewhart SN control chart on a real data set collected in a beverage company.     

基于标准化变换的质量控制图研究

基于标准化变换的质量控制图一般应用在小批量环境下,对生产过程进行控制。分析标准化变换的准则,指出工序分析起到关键作用及能否对工序进行控制,不在于统计,而在于管理;通过对不同数据变换方式有效性的研究表明,各种标准化变换控制图的复杂性及灵敏性是不同的,因此要依工序分析的结果灵活采用各种标准化控制图。     

常规控制图技术及应用研究

常规控制图是监控、分析生产过程处于稳态的有效手段和工具,也是一种有效的质量管理工具。归纳总结了常规控制图的设计程序和要点,重点论述和研究了在现代企业中运用常规控制图解决质量问题的方法和对策,为企业现场专业技术人员正确使用常规控制图提供了非常有用的经验和应用指南。     

Automatic Control Chart for Micropipet

Abstract

The control chart is defined and exemplified, and the importance of controlling the volume delivered by a micropipet is discussed. A LabVIEW VI is described, which automates control chart generation and routine use based on the gravimetric calibration of a micropipette, using a Sartorius balance. A user selects between creating a new control chart and adding a data point to the control chart. File storage and retrieval, and construction of the control chart are automated. A sample control chart for a micropipet is presented.     

小批量产品生产过程统计质量控制模型研究

在分析研究现有小批量统计质量控制相关理论的基础上,基于贝叶斯原理,提出了一种综合解决小批量生产过程质量控制的建模方法,此方法在保证第一类错误较小的前提下有效地减小第二类错误的发生,即在误发警报率尽量小的前提下减小漏发警报的发生。     

SXC control chart

This article proposes the SXC (sum of xs with curtailment) control chart. This chart is nearly as simple as the chart for the operators to understand and implement. Meanwhile, it is as effective as the CUSUM chart for detecting the process mean shifts. The SXC chart can be applied to random inspection, as well as its special cases, i.e. uniform and 100% inspections. It is found that the SXC chart can reduce the out-of-control average time to signal (ATS) by 74%, on average, over a wide range of mean shifts compared to the Shewhart chart.     

Statistical design of variable sample size and sampling interval $\bar{X}$\bar{X}control charts with run rules

In this paper, improved Shewhart control charts based on hybrid adaptive and run rule schemes are introduced to enhance the statistical performances of the traditional static scheme, designed with consideration given to the fixed values of sample size, the width of the control limits and the sampling frequency. The proposed hybrid adaptive schemes consider both variable sampling interval and variable sample size combined with run rules. The objective of this research is to develop a statistical comparison between adaptive schemes, charts with run rules and hybrid adaptive schemes with run rules to help decision-makers in the selection of the best performing chart for an expected value of shift in the mean of a controlled parameter. An extensive set of numerical results is presented to test the effectiveness of the proposed models in detecting small and moderate shifts in the process mean. The optimal statistical designs of the charts are obtained through a heuristic algorithm, properly modified to cope with the problem.