基于成本控制的小批量生产工序能力指数研究

传统的工序能力指数分析方法仅仅从统计质量管理的角度对过程能力进行分析,而忽略了所加工产品的不同批量、不同价值、不同加工工序成本的影响。对国内外多品种、小批量的工序能力指数研究现状进行了回顾,在对工序能力指数的点估计及置信区间进行分析的基础上,提出了基于成本控制的多品种、小批量生产方式下确定工序能力指数的方法,从而达到降低生产加工过程成本的目的。     

基于过程能力指数的抽样风险研究

如果将生产过程中上一道工序的加工过程（设备）视为生产者,而紧邻的下一道工序的加工过程（设备）视为使用者,则上道工序加工的零部件在通过质量抽样检验进入下道工序的过程中．就将产生抽样风险。而由抽样风险确定的抽样方案直接关系到加工过程质量控制和检验成本控制等加工效率问题。考虑到许多大批量自动生产线的检验设备已经具备在线计算过程能力指数的能力．提出了一个基于过程能力指数的抽样风险分析方法,通过计算过程能力指数,得到相应的抽样风险．进而确定合理的抽样检验方案,从而将加工过程的重要指标——过程能力指数与抽样检验方案之间建立了联系。最后,以某汽车发动机曲轴生产线加工过程为例进行了抽样风险分析。     

基于熵权和模糊物元的改进多元过程能力分析

为解决多品种小批量产品数据采集困难及各指标间存在交互影响致使传统方法难以奏效的现状,基于主成分分析法的多元过程能力指数会造成信息量缺损。该方法将工序能力影响因素采用模糊物元方法构造工序相似条件,选出相似工序将小批量问题变成大批量处理,应用公差系数法将原始数据转化为相对数据。提出基于熵权理论和模糊物元的改良多元过程能力指数。该论文结合某产品进行实例分析,验证了该方法的实用性和有效性。     

卷包在线质量控制系统评定方法探讨

过程能力指数是过程能力大小的一种定量描述，通过对过程能力指数的计算和判断，就能够定量的对卷包在线质量控制系统进行评定，从而判断卷包在线质量控制系统对产品质量的控制能力。     

基于质量指数的采购质量控制方法研究

质量是企业获取竞争优势的要素之一,采购管理的质量控制将决定企业能否成功地提供优质的产品和服务。文章提出了质量指数（PQI）指标,以反映供应商的产品质量控制能力,并且根据产品质量数据动态的评价供应商的质量控制能力,为企业选择供应商提供有力的依据。     

产品特性过程能力波动研究的新方法

本文介绍美国波音公司“先进质量体系”（ＡＱＳ）中对产品过程能力波动研究的新方法，提出能力指数Ｃｐｋ的新概念，它能更合理、更全面地反映系统、随机因素对过程能力的影响，还给出了各种特定情况下的Ｃｐｋ值的计算方法，进而引伸出满足产品关键特性过程能力指数Ｃｐｋ值的最低界限，为实施产品过程控制，推进连续质量改进提供了科学有效的途径。     

多元质量特性的过程能力指数

多元质量特性过程能力指数是一个尚未得到很好解决的问题。本文利用主成分分析，给出了计算多元质量特性过程能力指数的一种新方法，实证分析表明这种方法是可行的。     

基于不同质量损失函数的过程能力指数比较

通过比较Taguchi过程能力指数Cpm和Cpmk不同于传统的指数Cp和Cpk,指出Taguchi指数反映的是过程输出满足质量损失要求而非合格率的能力;提出均值和标准差变化对不同指数有着不同的影响,指数Cpk比Cpm对标准差的变化更敏感,指数Cpmk对均值的变化最敏感;分析了指数Cpk与合格率的近似对应关系,Taguchi指数与合格率没有确定的一一对应关系,但存在对应区间.通过讨论认为,实践中选用过程能力指数反映了组织的不同质量目标--短期合同要求还是长期"卓越质量".     

工序能力分析与评价中的几个问题

在工序质量分析与控制中,计算与评价工序能力指数是一项非常重要的工作,也是计算机辅助质量系统的一个重要模块。文章针对目前在工序能力计算与分析中出现的问题,提出了如何合理地进行抽样、样本数据的正态性检验以及对非正态性数据的处理、Ｃｐ的置信区间以及与样本含量的关系,旨在为实际生产过程中质量工程师进行工序能力分析和评价提供指导。     

基于服务蓝图技术的服务企业过程能力指数研究

对服务质量的控制与评价已经成为服务企业应对竞争、提高顾客满意度和忠诚度的关键。通过借鉴工业产品质量控制中过程能力指数的内涵,提出了服务企业过程能力指数的概念。利用服务蓝图技术描绘服务企业的业务流程,寻找所有内部服务接触点,确定内部服务评价的主客体关联图。在此基础上利用SERVQUAL服务质量测量标尺,设计服务流程中各环节服务质量评价的指标体系和调查问卷,得到各相关部门内部服务质量评价结果,通过专家赋权法给出各职能部门相对整体服务质量绩效的重要性权重,并对各部门的评价结果进行加权综合,从而得到服务企业过程能力指数,为评价内部服务质量、分析质量问题产生根源打下基础。最后以某市区供电公司为例,详细说明了服务企业过程能力指数的生成过程。     

The development of a target‐focused process capability index with multiple characteristics

Within an industrial manufacturing environment, Process Capability Indices (PCIs) enable engineers to assess the process performance and ultimately improve the product quality. Despite the fact that most industrial products manufactured today possess multiple quality characteristics, the vast majority of the literature within this area primarily focuses on univariate measures to assess process capability. One particular univariate index, C_pm, is widely used to account for deviations between the location of the process mean and the target value of a process. While some researchers have sought to develop multivariate analogues of C_pm, modeling the loss in quality associated with multiple quality characteristics continues to remain a challenge. This paper proposes a multivariate PCI that more appropriately estimates quality loss, while offering greater flexibility in conforming to various industrial applications, and maintaining a more realistic approach to assessing process capability. Copyright © 2010 John Wiley & Sons, Ltd.     

Integrating Customer Perception into Process Capability Measures

Process capability indices provide a measure of the output of an in‐control process that conforms to a set of specification limits. These measures, which assume that process output is approximately normally distributed, are intended for measuring process capability for manufacturing systems. When the performance of a system results in a product that fails to fall within a given specification range, however, the product is typically scrapped or reworked, and the actual distribution that the customer perceives after inspection is truncated. In this paper, the concept of a truncated measure for three types of quality characteristics is introduced as the key to linking customer perception to process capability. Subsequently, a set of customer‐perceived process capability indices is presented as an extension of traditional manufacturer‐based counterparts. Finally, data transformation‐based process capability indices are also discussed. A comparative study and numerical example reveal considerable differences among the traditional and proposed process capability indices. It is believed that the proposed process capability index for various quality characteristics may more aptly lead to process improvement by facilitating a better understanding of the integrated effects found in engineering design problems. Copyright © 2015 John Wiley & Sons, Ltd.     

Process Capability Analysis for NonLinear Profiles Using Depth Functions

There are practical situations in which the quality of a process or product can be better characterized by a functional relationship between a response variable and one or more explanatory variables, which is called profile. Such profiles frequently can be represented adequately using linear or nonlinear models. While there are several studies in monitoring profiles, there are few studies to evaluate the capability of a process with profile quality characteristic; specifically, there is no method in the literature to analyze process capability characterized by nonlinear profiles. In this paper, we propose two methods to measure the capability of these processes, based on the concept of functional depth. These methods do not have distributional assumptions and extend to functional data the Process Capability Indexes proposed by Clements 1 to measure the capability of a process characterized by a random variable. Performance of the proposed methods is evaluated through simulation studies. An example illustrates the applicability of these methods. Copyright © 2013 John Wiley & Sons, Ltd.     

Process capability indices—an overview of theory and practice

A range of process capability indices is widely used to measure process performance. The simplicity of the formulae for these capability indices is both a strength and a weakness. The underlying assumptions behind capability indices are frequently overlooked. Capability studies usually result in single point estimates which may result in misleading assessments of process performance. Point estimates ignore sampling error, and safer estimates can be obtained by constructing confidence intervals. The construction of confidence intervals is considered in some detail. Testing or measurement variability give rise to additional uncertainty in process capability assessments. Inaccurate assessments of process performance can result if the basic assumptions and sources of uncertainty are overlooked.     

Yield‐Based Capability Index for Evaluating the Performance of Multivariate Manufacturing Process

Process capability indices (PCIs) have been widely used in the manufacturing industry providing numerical measures on process precision, accuracy and performance. Capability indices measures for processes with a single characteristic have been investigated extensively. However, an industrial product may have more than one quality characteristic. In order to establish performance measures for evaluating the capability of a multivariate manufacturing process, multivariate PCIs should be introduced. In this paper, we analyze the relationship between PCI and process yield. The PCI EC_pk is proposed based on the idea of six sigma strategy, and there is a one‐to‐one relationship between EC_pk index and process yield. Following the same, idea we propose a PCI MEC_pk to measure processes with multiple characteristics. MEC_pk index can evaluate the overall process yield of both one‐sided and two‐sided processes. We also analyze the effect of covariance matrix on overall process yield and suggest a solution for improving overall process yield. Copyright © 2013 John Wiley & Sons, Ltd.     

Univariate and multivariate process yield indices based on location-scale family of distributions

Several measures of process yield, defined on univariate and multivariate normal process characteristics, have been introduced and studied by several authors. These measures supplement several well-known Process Capacity Indices (PCI) used widely in assessing the quality of products before being released into the marketplace. In this paper, we generalise these yield indices to the location-scale family of distributions which includes the normal distribution as one of its member. One of the key contributions of this paper is to demonstrate that under appropriate conditions, these indices converge in distribution to a normal distribution. Several numerical examples will be used to illustrate our procedures and show how they can be applied to perform statistical inferences on process capability.     

常闭阀罩生产过程质量控制研究

为了解决某公司常闭阀罩生产过程中质量不稳定、生产过程能力低等问题,将质量管理方法应用于常闭阀罩生产的管理过程。利用测量系统分析、过程能力分析、一元线性回归分析等方法进行产品质量的分析,并结合Minitab 软件里面的功能模块,精确计算过程能力指数,生成控制图,根据判定准则,给出判定结论。企业应用实例表明,该公司在实施了质量控制后,过程能力提高,废品率降低,有效地提高了产品质量。     

Process True Capability Evaluation with the Consideration of Measurement System Variability and Expected Quality Loss

Process capability indices are widely computed under the assumption that the measurement system is free from errors. However, measurement variability is unavoidable and has a significant impact in process capability evaluation. From an economic point of view, Taguchi loss function is an effective tool to measure the quality loss of a product characteristic deviated from target value that is extensively used without taking into account the effect of the measurement system. This paper investigates the influence of measurement system variability on the process capability analysis through the calculation of process capability indices. A new quality loss function, integrating the measurement system errors, is developed to compute the optimal true process capability regarding to the expected mean value of the Taguchi loss function and the loss resulting from the control of the true process capability. Copyright © 2016 John Wiley & Sons, Ltd.     

Evaluation of Nonnormal Process Capability Indices using Generalized Lambda Distribution

Process capability indices (PCIs) are used to describe a manufacturing process expressing its ability to produce items within the specified limits. These indices are developed under the assumption that the underlying process distribution is normal. In industries, there are many manufacturing processes where process distribution can not be described by a normal distribution. In such cases, those PCIs will give misleading results about the process. The most commonly used approach for analysing a nonnormal process data is to fit a standard nonnormal distribution (e.g., weibull, gamma) or a family of distribution curves (e.g., Pearson, Johnson) to the process data and then to estimate the percentile points from the fitted distribution that can be used to compute generalized PCIs. In this article, we outline the procedure using the generalized lambda distribution (GLD) curve for modeling a set of process data and for estimating percentile points in order to compute generalized PCIs. The four-parameter GLD can assume a wide variety of curve shapes and hence it is very useful for the representation of data when the underlying model is unknown. Compared to the Pearson and Johnson family of distributions, the GLD is computationally simpler and more flexible. The article provides all necessary formulas for fitting a GLD curve, estimating its parameters, performing goodness-of-fit tests, and computing generalized PCIs. An example is used to illustrate the calculations that can be easily performed using spreadsheets.