共查询到20条相似文献，搜索用时 15 毫秒
1.
B. M. Hsu M. H. Shu W. L. Pearn 《Quality and Reliability Engineering International》2007,23(5):597-614
Due to their effectiveness and simplicity of use, the process capability indices , , and have been popularly accepted in the manufacturing industry as management tools for evaluating and improving process quality. Combining the merits of those indices, a more advanced index, , is proposed that takes into account process variation, process centering, and the proximity to the target value, and has been shown to be a very useful index for manufacturing processes with two‐sided specification limits. Most research works related to assume no gauge measurement errors. However, such an assumption inadequately reflects real situations even when highly advanced measurement instruments are employed. Conclusions drawn regarding process capability are therefore unreliable and misleading. In this paper, we conduct a sensitivity investigation for the process capability index in the presence of gauge measurement errors. We consider the use of capability testing of as a method for obtaining lower confidence bounds and critical values for true process capability when gauge measurement errors are unavoidable. The results show that using the estimator with sample data contaminated by measurement errors severely underestimates the true capability, resulting in an imperceptibly smaller test power. To measure the true process capability, three methods for the adjusted confidence bounds are presented and their performances are compared using computer simulation. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
2.
W. L. Pearn C. H. Wu M. C. Tsai 《Quality and Reliability Engineering International》2013,29(2):159-163
The generalized yield index establishes the relationship between the manufacturing specifications and the actual process performance, which provides a lower bound on process yield for two‐sided processes with multiple characteristics. The results attended are very practical for industrial application. In this article, we extended the results in cases with one‐sided specification and multiple characteristics. The generalized index was considered, and the asymptotic distribution of the natural estimator was developed. Then, we derived the lower confidence bounds as well as the critical values of index . We not only provided some tables but also presented an application example. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
3.
Fu‐Kwun Wang 《Quality and Reliability Engineering International》2014,30(8):1145-1151
Profile monitoring is mainly for checking the stability of the relationship between response and explanatory variables over time based on observed data. Linear profiles are common in calibration applications. In this study, we develop two new indices for measuring the process yield for simple linear profiles with one‐sided specification. The asymptotic distribution of the estimated index is provided. The approximate lower confidence bound for the true process yield is also obtained and used to determine whether the process yield meets the quality requirement. A simulation study is conducted to assess the performance of the proposed method. The results show that the coverage rates of the confidence intervals for all simulated cases are greater than the 95% lower limit of the stated nominal value. One real example is used to illustrate the applicability of the proposed approach. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
4.
Fu‐Kwun Wang Yeneneh Tamirat 《Quality and Reliability Engineering International》2016,32(4):1281-1293
The new investigation of profile monitoring is focused mainly on a process with multiple quality characteristics. Process yield has been used widely in the manufacturing industry, as an index for measuring process capability. In this study, we present two indices and to measure the process capability for multivariate linear profiles with one‐sided specification limits under mutually independent normality. Additionally, two indices and are proposed to measure the process capability for multivariate linear profiles with one‐sided specification limits under multivariate normality. These indices can provide an exact measure of the process yield. The approximate normal distributions for and are constructed. A simulation study is conducted to assess the performance of the proposed approach. The simulation results show that the estimated value of performs better as the number of profiles increases. Two illustrative examples are used to demonstrate the applicability of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
5.
Majid Jalili Mahdi Bashiri Amirhossein Amiri 《Quality and Reliability Engineering International》2012,28(8):925-941
Process capability indices (PCIs) are used in statistical process control to evaluate the capability of the processes in satisfying the customer's needs. In the past two decades varieties of PCI are introduced by researchers to analyze the process capability with univariate or multivariate quality characteristics. To the best of our knowledge, most famous multivariate capability indices are proposed when the quality characteristics have both upper and lower specification limits. These indices are incapable to assess the multivariate processes capability with unilateral specification. In this article, we propose a new multivariate PCI to analyze the processes with one or more unilateral specification limits. This new index also accounts for all problems in the best PCIs of the literature. The performance of the proposed index is evaluated by real cases under different situations. The results show that the proposed index performs satisfactorily in all cases considered. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
6.
Krzysztof Ciupke 《Quality and Reliability Engineering International》2015,31(2):313-327
To evaluate the capability of manufacturing processes in satisfying the customer's needs, a variety of indices has been developed. Some of them are introduced by researchers to analyse the processes with multivariate quality characteristics. Most of the proposed in the literature multivariate capability indices are defined under assumption of normality distribution of the quality characteristics. Thus, the process region describing the variation of the data has an elliptical shape. In this paper, a multivariate process capability vector with three components is introduced, which allows to access the capability of a process with both normally and non‐normally quality characteristics due to application of a pair of one‐sided models as the process region shape. At the beginning, one‐sided models are defined, next the proposed vector components are proposed and the methodology of their evaluation is presented. The methodology (which in fact could be also applied to both the correlated and non‐correlated characteristics) is verified by applying simulation and real problems. The obtained results show that the proposed methodology performs satisfactorily in all considered cases. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
7.
W. L. Pearn C. H. Wu H. N. Hung C. M. Kao 《Quality and Reliability Engineering International》2013,29(2):277-284
In statistical quality control, product acceptance determination is an important problem for producer and consumer. In practical, particularly more than one quality characteristic must be simultaneously considered to improve the product quality because of the product design. In this article, we investigate the lot sentencing problem for normally distributed process with one‐sided specification and multiple characteristics. We not only provide a simple procedure to help practitioners make reliable decision easily but also tabulate the required sample size and the corresponding critical acceptance value for various producer's and consumer's risks with the capability requirements AQL (acceptable quality level) and LTPD (lot tolerance percent defective). Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
8.
Fu‐Kwun Wang 《Quality and Reliability Engineering International》2016,32(1):257-268
When the quality of a process is represented by a relationship between one response variable and one or more independent variables called a multiple linear profile with in statistical control, the process capability analysis is widely used to measure the capability of the process to manufactured item within the required tolerance. In this paper, we propose the difference test statistic to compare two processes for multiple linear profiles with one‐sided specifications. The number of profiles required for a designated selection power and confidence level is also provided. The performance of the proposed method is assessed using simulation study. The results provide useful information to practitioners. A real data from the logistic service shows that our method performs well in the application. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
9.
Silvano Bordignon Michele Scagliarini 《Quality and Reliability Engineering International》2006,22(7):787-801
In this paper we study the properties of the estimator of Cpm when the observations are affected by measurement errors. We compare the performances of the estimator in the error case with those of the estimator in the error‐free case. The results indicate that the presence of measurement errors in the data leads to different behavior of the estimator according to the entity of the error variability. We finally show how to use our results in practice. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
10.
Process capability indices such as Cp, Cpk, Cpmk and Cpm are widely used in manufacturing industries to provide a quantitative measurement of the performance of the products. In this article, we derived generalized confidence intervals for the difference between process capability indices for two processes under one‐way random effect model. Our study provides coverage probability close to the nominal value in almost all cases as shown via simulation. An example from industrial contexts is given to illustrate the results. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
11.
Jyh‐Jen Horng Shiau Chia‐Ling Yen W. L. Pearn Wan‐Tsz Lee 《Quality and Reliability Engineering International》2013,29(4):487-507
Process capability indices (PCIs) have been widely used in industries for assessing the capability of manufacturing processes. Castagliola and Castellanos (Quality Technology and Quantitative Management 2005, 2(2):201–220), viewing that there were no clear links between the definition of the existing multivariate PCIs and theoretical proportion of nonconforming product items, defined a bivariate Cpk and Cp (denoted by BCpk and BCp, respectively) based on the proportions of nonconforming product items over four convex polygons for bivariate normal processes with a rectangular specification region. In this paper, we extend their definitions to MCpk and MCp for multivariate normal processes with flexible specification regions. To link the index to the yield, we establish a ‘reachable’ lower bound for the process yield as a function of MCpk. An algorithm suitable for such processes is developed to compute the natural estimate of MCpk from process data. Furthermore, we construct via the bootstrap approach the lower confidence bound of MCpk, a measure often used by producers for quality assurance to consumers. As for BCp, we first modify the original definition with a simple preprocessing step to make BCp scale‐invariant. A very efficient algorithm is developed for computing a natural estimator of BCp. This new approach of BCp can be easily extended to MCp for multivariate processes. For BCp, we further derive an approximate normal distribution for , which enables us to construct procedures for making statistical inferences about process capability based on data, including the hypothesis testing, confidence interval, and lower confidence bound. Finally, the proposed procedures are demonstrated with three real data sets. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
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Process yield is an important criterion used in the manufacturing industry for measuring process performance. Methods for measuring yield for processes with single characteristic have been investigated extensively. However, methods for measuring yield for processes with multiple characteristics have been comparatively neglected. In this paper, we develop a generalized yield index, called TS pk,PC , based on the index Spk introduced by Boyles (Journal of Quality Technology, 23, 17–26, 1991) using the principal component analysis (PCA) technique. We obtained a lower confidence bound (LCB) for the true process yield. The proposed method can be used to determine whether a process meets the preset yield requirement, and make reliable decisions. Examples are provided to demonstrate the proposed methodology. 相似文献
14.
Malin Albing 《Quality and Reliability Engineering International》2009,25(3):317-334
We consider a previously proposed class of capability indices that are useful when the quality characteristic of interest has a skewed, zero‐bound distribution with a long tail towards large values and there is an upper specification with a pre‐specified target value, T=0. We investigate this class of process capability indices when the underlying distribution is a Weibull distribution and focus on the situation when the Weibull distribution is highly skewed. We propose an estimator of the index in the studied class, based on the maximum likelihood estimators of the parameters in the Weibull distribution, and derive an asymptotic distribution for this estimator. Furthermore, we suggest a decision rule based on the estimated index and its asymptotic distribution and present a power comparison between the proposed estimator and a previously studied estimator. A simulation study is also performed to investigate the true significance level when the sample size is small or moderate. An example from a Swedish industry is presented. Copyright © 2008 John Wiley & Sons, Ltd. 相似文献
15.
Fu‐Kwun Wang Yeneneh Tamirat Yuan‐Sheng Tsai 《Quality and Reliability Engineering International》2015,31(8):1575-1585
The quality of a process is characterized by a functional relationship between the response variable and one or more explanatory variables called a linear profile. In this study, the process selection problem that deals with comparing two linear profiles with one‐sided specifications and selecting the one that has a significantly higher capability value is presented. An exact approach based on the ratio test statistic to tackle the supplier selection problem is proposed. Testing hypotheses with two cases for comparing two processes is considered. Critical values of the tests are calculated to determine the selection decisions. The number of profiles required for a designated selection power and confidence level is also provided. The results provide useful information to practitioners. One real example is used to illustrate the application of our proposed approach. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
16.
Thomas Mathew G. Sebastian K. M. Kurian 《Quality and Reliability Engineering International》2007,23(4):471-481
The concept of generalized confidence intervals is used to derive lower confidence limits for some of the commonly used process capability indices. For the cases where approximate lower confidence limits are already available, numerical comparisons are made among the available approximations and the generalized lower confidence limit. The numerical results indicate that the generalized confidence interval does provide coverage probabilities very close to the nominal confidence level. Two examples are given to illustrate the results. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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18.
Michael Perakis Evdokia Xekalaki 《Quality and Reliability Engineering International》2004,20(7):651-665
In the statistical literature on the study of the capability of processes through the use of indices, Cpm appears to have been one of the most widely used capability indices and its estimation has attracted much interest. In this article, a new method for constructing approximate confidence intervals or lower confidence limits for this index is suggested. The method is based on an approximation of the non‐central chi‐square distribution, which was proposed by Pearson. Its coverage appears to be more satisfactory compared with that achieved by any of the two most widely used methods that were proposed by Boyles, in situations where one is interested in assessing a lower confidence limit for Cpm. This is supported by the results of an extensive simulation study. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
19.
Fu‐Kwun Wang 《Quality and Reliability Engineering International》2016,32(7):2435-2442
Process yield plays an important role in many manufacturing industries for measuring process performance. However, gauge measurement errors have significant effect on process capability analysis. In this study, we present a method based on the yield index to evaluate the process yield of nonlinear profiles in the presence of gauge measurement errors. The results indicate that the presence of gauge measurement errors in the data leads to different behaviors of the yield index estimator according to the existence of the gauge variability. Our proposed test procedure can be easily used to determine whether or not manufacturing processes meet the quality requirements when gauge measurement errors are considered. A real example from a manufacturing process is used to demonstrate the applications of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
20.
Silvano Bordignon Michele Scagliarini 《Quality and Reliability Engineering International》2002,18(4):321-332
Process capability indices (PCIs) have been widely used in manufacturing industries to provide a quantitative measure of process potential and performance. While some efforts have been dedicated in the literature to the statistical properties of PCIs estimators, scarce attention has been given to the evaluation of these properties when sample data are affected by measurement errors. In this work we deal with the problem of measurement‐error effects on the performance of PCIs. The analysis is illustrated with reference to and , i.e. the two most common measures suggested to evaluate process capability. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献