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Kuen-suan Chen - One of the best experts on this subject based on the ideXlab platform.
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A measuring model of Process Capability to consider sampling error
Journal of Information and Optimization Sciences, 2009Co-Authors: Kuen-suan Chen, Liang-yuh Ouyang, Chang-hsien HsuAbstract:Most products have multiple characteristics, and customers accept products whenever all Process capabilities of each characteristic satisfy preset specifications. Obviously, univariate Process Capability indices cannot meet the requirements stated as above. And Chen et al. (2001) used point estimation to evaluate Process Capability, even though the method agrees with 100% tests. If the check is sampling, the Process Capability must consider sampling error. So the research constructs a measuring model of Process Capability to consider sampling error to evaluate Process Capability for a multi-Process produce based on Cpk which was proposed by Kane (1986).
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Process Capability analysis chart with the application of Cpm
International Journal of Production Research, 2008Co-Authors: Kuen-suan Chen, M L Huang, Y. H. HungAbstract:Process Capability analysis (PCA) is a highly effective means of assessing the Process ability of product that meets specifications. The Process Capability analysis chart (PCAC/Cpk ) evaluates the capabilities of multiProcess products together with nominal-the-best specifications, larger-the-better and smaller-the-better specifications. This study proposes Process Capability analysis chart (PCAC/Cpm ) to consider Process yield and expected Process loss. A new generated estimator for Cpm is proposed and the properties of statistical estimator and hypothesis test are discussed. A practical example was given for application.
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Multi-Process Capability plot and fuzzy inference evaluation
International Journal of Production Economics, 2008Co-Authors: Kuen-suan Chen, T.w. ChenAbstract:Abstract Process Capability indices Cp, Cpk, Cpm and Cpp fitting for nominal-the-best type quality characteristics, are effective tools to assess Process Capability since these indices can reflect a centering Process Capability and Process yield adequately. The index Cpp introduced by Greenwith and Jahr-Schaffrath [Greenwith, M., Jahr-Schaffrath, B.L., 1995. A Process inCapability index. International Journal of Quality and Reliability Management 12, 58–71] provides additional and individual information concerning the Process accuracy and the Process precision. Although Cpp is useful to evaluate Process Capability for a single product in common situation, Cpp cannot be applied to evaluate the multi-Process Capability. Referring to Vannman and Deleryd's (Cdr, Cdp)-plot, a fuzzy inference approach is proposed in our study to evaluate the multi-Process Capability based on distance values of a confidence box. This method takes the advantages of fuzzy systems such that a grade instead of sharp evaluation result can be obtained. An illustrated example of color STN display demonstrates that the presented method is effective for assessment of multi-Process Capability.
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The communion bridge to Six Sigma and Process Capability indices
Quality and Quantity, 2007Co-Authors: Kuen-suan Chen, Liang-yuh Ouyang, C. H. HsuAbstract:Six Sigma has already become an efficient improvement technique adopted by a great number of enterprises. Numbers of Sigma has become a tool of measuring Process Capability in some enterprises. But some of enterprises still use Process Capability indices (PCIs) to measure the Process Capability. So numbers of Sigma and PCIs both can be used to measure the Process Capability. The paper will research the relationship between PCIs and numbers of Sigma. In bilateral specifications, the paper will research the relationship between the PCIs which are Cp, Cpk, Cpm and Cpmk, Spk and numbers of Sigma. In unilateral specifications, the paper will research the relationship between the PCIs which are Cpu and Cpl and numbers of Sigma. If supplier and buyer use different tools to measure the Process Capability, then the communion bridge to Six Sigma and PCIs can decrease the communicate noise.
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The evaluation of Process Capability for a machining center
The International Journal of Advanced Manufacturing Technology, 2007Co-Authors: K. T. Yu, Shey-huei Sheu, Kuen-suan ChenAbstract:The machining center methodology is widely applied to production systems within the fast-developing Processing industry. The automated machining center can simultaneously perform certain Processes, such as milling, drilling, boring, and reaming, on the same machine at the same time, and manufactures limited quantities of multiple-type products. These products usually have numerically important quality characteristics with high accuracy. However, a machining center is unable to measure Process Capability on its own. Thus, we reflect the Process Capability of a machining center by measuring the quality characteristics of its Processing products. In this paper, we propose the three integrated Process Capability indices (PCIs) C p mc , C a mc , and C pm mc to evaluate the integrated Process precision, accuracy, and actual Capability respectively. Furthermore, we develop a Process Capability monitoring figure (PCMF), which not only displays the status of the Process precision and accuracy by the color management method [1], but it also forecasts the integrated Process Capability of the next productive time (batch) through the analysis of time series. Using the PCMF, engineers will be assisted with tasks such as monitoring the Process quality, deciding the period of the borer’s replacement, and designing the Process parameters.
Hamid Shahriari - One of the best experts on this subject based on the ideXlab platform.
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a Process Capability index for simple linear profile
The International Journal of Advanced Manufacturing Technology, 2013Co-Authors: Mohsen Ebadi, Hamid ShahriariAbstract:In some cases, the quality of a Process or a product is characterized by a linear regression model which is also called a linear profile. Process Capability index is an important concept in statistical quality control and measures the ability of a Process to manufacture products that meet certain specifications. There has been little attempt to study the Process Capability in linear profiles. In this paper, two methods are proposed for measuring the Process Capability in simple linear profiles. The first method uses the percentage of nonconforming parts produced at each level of the independent variable to introduce a Process Capability index. The second method is a multivariate Process Capability approach where a vector of three components is introduced. The components of the vector assess the Process dispersion, its centrality, and its location within the upper and the lower specification limits. In comparison to the only existing method, numerical analysis based on simulation studies indicates that the suggested Process Capability measurements perform more accurately in evaluating the Capability of a Process generating a simple linear profile. When the error term variance is 1 and the actual percentage of nonconformities is 0.11 %, the proposed method estimates 0.11 % nonconforming percentage and a Capability index of 1.06. While the existing method results in 0.27 % nonconforming percentage and a Capability index of 0.9, the existing method underestimates the Process Capability.
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A New Multivariate Process Capability Vector
Quality Engineering, 2009Co-Authors: Hamid Shahriari, Mohammadreza AbdollahzadehAbstract:There has been little research devoted to Process Capability indices for Processes with multiple quality characteristics. This research discusses the concept of Process Capability and its relevant indices in univariate and multivariate cases and introdu..
Mohammad Abdolshah - One of the best experts on this subject based on the ideXlab platform.
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Development Of A Fuzzy Loss-based Process Capability Index
2013Co-Authors: Mohammad AbdolshahAbstract:Process Capability indices are appropriate tools in order to measure the inherent Capability of Process, but most of these indices cannot take into account the losses of a Process such as rejects, while in today’s competitive business environment, it is becoming more and more important for companies to evaluate and minimize their losses. Since most of Process Capability indices do not consider the losses, the Process Capability indices based on losses can help manufacturers to understand the real Capability of their Processes in order to improve them. Literature review showed two main gaps in loss-based Process Capability indices. The first gap is that there is not a loss-based Process Capability index, which has more features such as reject based, asymmetric, bounded, and target based. In order to overcome this problem, an appropriate loss function (asymmetric inverted normal loss function) was employed to propose a new loss-based Process Capability index. The methodology is to compare the standard loss for a capable Process with other cases. The proposed Process Capability index is bounded, asymmetric and it is able to provide a more realistic metric to evaluate and predict the performance of Processes. The second gap in literature review is that among all loss-based Process Capability indices, just Cpm has been fuzzificated, while literature review showed that the index Cpm is not an appropriate Process Capability index. Then fuzzy logic and operation research model were employed to fuzzificate the new proposed loss-based Process Capability index. The α-cuts of the fuzzy observation was the method employed to find the fuzzy membership function of the new loss-based Process Capability indices. The result of this study is a new loss-based index with more specifications such as mean-based, target- based, variation-based, bounded, and loss-based compared with other Process Capability indices. The new Process Capability index was fuzzificated using α-cut method. Therefore a fuzzy loss-based Process Capability index was developed that is useful for vague data. In order to validate the new loss-based method, the sensitivity of this index to Process specifications was studied. Sensitivity analysis showed that this index is sensitive to the mean, variation, and target of data. The new index has 99.6% relationship with the loss especially asymmetric inverted normal loss function. This relationship with loss is the highest relationship compared with other Process Capability indices. Moreover a regression analysis showed that the new index has the most relationship with the value of loss compared with other Process Capability indices.
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A fuzzy Taguchi loss-based Process Capability index
International Journal of Quality Engineering and Technology, 2013Co-Authors: Mohammad AbdolshahAbstract:Process Capability indices are appropriate tools to measure the inherent Capability of a Process, but most of them do not take into account the losses of a Process. By integrating the Taguchi loss function and a Process Capability index (Cpk), a new Process Capability index called fuzzy Taguchi loss-based Process Capability index (FTPCI) was developed. Due to the definitions of a capable Process or value of standard loss, the fuzzy approach to the result of the index Cpk and Taguchi losses have been proposed. In order to construct this new fuzzy Process Capability index, Mamdani fuzzy system consisting of the Taguchi loss and the index Cpk was employed. Finally, an example on the usage of this Process Capability index illustrated the results.
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Fuzzy Process Capability Indices: A Review
2012Co-Authors: Mohammad AbdolshahAbstract:Process Capability Indices (PCIs) are appropriate tools in order to measure the inherent Capability of a Process. In statistical Process control, there are some uncertainties in data, such as uncertain specification limits and data. In these cases, fuzzy logic can be employed to manage the uncertainties. There are some researches about fuzzy Process Capability indices and in this paper; we present a review of fuzzy Process Capability indices and the related methodologies for fuzzification. Finally, recommendations are made to fuzzificate other Process Capability indices with more excellent specifications.
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Measuring Process Capability index Cpmk with fuzzy data and compare it with other fuzzy Process Capability indices
Expert Systems with Applications, 2011Co-Authors: Mohammad Abdolshah, Rosnah Mohd Yusuff, Tang Sai Hong, Yusof Ismail, Aghdas Naimi SadighAbstract:The index Cpmk is a well-known loss-based Process Capability index. It can reveal more information about the location of the Process mean compared with other classic Process Capability indices. This index is also more sensitive than other Capability indices to any deviations from Process mean. When there are some uncertainties in observations, fuzzy logic can be employed to manage these uncertainties. There is some research on different fuzzy Process Capability indices and this paper is an extension of Tsai and Chen (2006), Chen, Lin, and Chen (2003) for the Process Capability index Cpmk of fuzzy numbers. In order to find the membership function of Process Capability index Cpmk, the α-cuts of the fuzzy observation were employed. An example of fuzzy Process Capability Cpmk calculator was illustrated and compared with other classic fuzzy Process Capability indices C ~ p , C ~ pl , C ~ pu , C ~ pk and C ~ pm . Results showed that fuzzy C ~ pmk has the advantages of both C ~ pk and C ~ pm . Since the index Cpmk is a more sensitive index compared with other classic indices, the fuzzy Process Capability index C ~ pmk can be a more sensitive fuzzy index compared with C ~ p , C ~ pl , C ~ pu , C ~ pk and C ~ pm .
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New Process Capability index using Taguchi loss functions
Journal of Applied Sciences, 2009Co-Authors: Mohammad Abdolshah, Rosnah Mohd Yusuff, Sai Hong Tang, Yusof IsmailAbstract:Classic Process Capability indices such as Ca, Cp and Cpk are well-known Process Capability indices, which are using widely. Since, Process Capability indices predict the Capability of a Process, they must have a significant relation with rate of rejects and losses. Studies showed that mostly Process Capability indices do not have a significant relation with rate of rejects or losses. Therefore, the loss-based indices are more appropriate and suitable indices to predict the Capability of a Process. In order to define a new loss-based Process Capability index, Taguchi loss functions were employed and this study proposed a novel Process Capability index called Taguchi-based Process Capability Index (TPCI). The methodology of this Process Capability index is based on standard rate of rejects for a capable Process compared to other cases. Therefore, this study develops a new Process Capability index, which is Taguchi loss function-based and sensitive to losses. This new Process Capability index can provide a realistic and applicable metric to evaluate a Process.
Kerstin Vännman - One of the best experts on this subject based on the ideXlab platform.
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Encyclopedia of Statistics in Quality and Reliability - Process Capability Plots
Wiley StatsRef: Statistics Reference Online, 2014Co-Authors: Kerstin VännmanAbstract:A Process is usually defined to be capable if a Process Capability index exceeds a stated threshold value. This definition can be expressed graphically as a region in the plane, defined by the Process parameters, to obtain a Process Capability plot. Under the assumption of normality, safety regions can be plotted in the Process Capability plot to draw conclusions about Process Capability at a given significance level. Alternatively, an estimated Process Capability plot defined by the estimators of Process parameters can be used. This article discusses these different kinds of plots and presents examples and interpretations. Keywords: Capability index; Process Capability plot; safety region; circular region; Capability region; normality assumption; graphical method; test
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Safety Regions in Process Capability Plots
Quality Technology & Quantitative Management, 2006Co-Authors: Kerstin VännmanAbstract:A Process is usually defined to be capable if the Process Capability index exceeds a stated threshold value, e.g., Cpm > 4/3. This inequality can be expressed graphically as a region in the plane defined by the Process parameters ( , µ σ ). In the obtained plot special regions can be plotted to test for Process Capability. These regions are similar to confidence regions for ( , µ σ ). This idea of using regions in Process Capability plots to assess the Capability is developed further for the Capability index Cpm. A new circular region is constructed that can be used, in a simple graphical way, to draw conclusions about the Capability of the Process at a given significance level. Using circular regions several characteristics with different specification limits and different sample sizes can be monitored in the same plot. Under the assumption of normality the suggested method is investigated with respect to power as well as compared to other existing graphical methods for drawing inference about Process Capability.
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Process Capability plots a quality improvement tool
Quality and Reliability Engineering International, 1999Co-Authors: Mats Deleryd, Kerstin VännmanAbstract:We introduce the concept of Process Capability plots, which are powerful tools to monitor and improve the Capability of industrial Processes. An advantage of using a Process Capability plot, compared with using a traditional Process Capability index alone, when deciding whether a Process can be considered capable or not, is that we will instantly get information about the location and spread of the studied characteristic. When the Process is non-capable, the plots are helpful when trying to understand if it is the variability, the deviation from target or both that need to be reduced to improve the Capability. In this way the proposed graphical methods give a clear direction of quality improvement. We evaluate two different Process Capability plots, the (δ*, γ*)-plot and the confidence rectangle plot, from a theoretical as well as a practical point of view. When studying them from a theoretical point of view, among other things, a simulation study is conducted to investigate the ability of each of the two methods to identify that a Process is capable when it actually is. The comparison from a practical point of view is made by discussing the advantages and disadvantages of the two methods in different practical situations. Based on the above-mentioned comparisons, the recommendation is that the practitioner should use the (δ*, γ*)-plot. Copyright © 1999 John Wiley & Sons, Ltd.
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Process Capability Studies for Short Production Runs
International Journal of Reliability Quality and Safety Engineering, 1998Co-Authors: Mats Deleryd, Kerstin VännmanAbstract:The current trend in modern production is directed towards shorter and shorter production runs. The two major reasons causing this trend are the rapid spread of the just in time (JIT) philosophy and the constantly increasing multiplicity of customer demands. The short runs of modern production not only constitute a challenge for production management, but they also cause some problems when applying traditional statistical methods, designed to be used for large sets of data. One of these methods is Process Capability studies. Since theories on how to use Process Capability studies in short production environments are incomplete, the aim of this paper is to present some ideas which will partly fill this gap. The theories of Process Capability studies for short runs presented are based on ideas of focusing on the Process, not on the products, and on using data transformation. By using the transformation presented, it is possible to conduct Process Capability studies in a traditional straightforward manner. A simulation study shows that the suggested transformation technique works satisfactorily in real situations. Finally, the -plot is introduced as a method of interpreting and analyzing the Capability of a short run production Process. By using the -plot, additional information is obtained concerning the Capability of a Process, compared to using traditional Process Capability indices only.
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Process Capability Studies for Short Production Runs
International Journal of Reliability Quality and Safety Engineering, 1998Co-Authors: Mats Deleryd, Kerstin VännmanAbstract:The current trend in modern production is directed towards shorter and shorter production runs. The two major reasons causing this trend are the rapid spread of the just in time (JIT) philosophy and the constantly increasing multiplicity of customer demands. The short runs of modern production not only constitute a challenge for production management, but they also cause some problems when applying traditional statistical methods, designed to be used for large sets of data. One of these methods is Process Capability studies. Since theories on how to use Process Capability studies in short production environments are incomplete, the aim of this paper is to present some ideas which will partly fill this gap. The theories of Process Capability studies for short runs presented are based on ideas of focusing on the Process, not on the products, and on using data transformation. By using the transformation presented, it is possible to conduct Process Capability studies in a traditional straightforward manner. A simulation study shows that the suggested transformation technique works satisfactorily in real situations. Finally, the [Formula: see text]-plot is introduced as a method of interpreting and analyzing the Capability of a short run production Process. By using the [Formula: see text]-plot, additional information is obtained concerning the Capability of a Process, compared to using traditional Process Capability indices only.
Mats Deleryd - One of the best experts on this subject based on the ideXlab platform.
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Process Capability plots a quality improvement tool
Quality and Reliability Engineering International, 1999Co-Authors: Mats Deleryd, Kerstin VännmanAbstract:We introduce the concept of Process Capability plots, which are powerful tools to monitor and improve the Capability of industrial Processes. An advantage of using a Process Capability plot, compared with using a traditional Process Capability index alone, when deciding whether a Process can be considered capable or not, is that we will instantly get information about the location and spread of the studied characteristic. When the Process is non-capable, the plots are helpful when trying to understand if it is the variability, the deviation from target or both that need to be reduced to improve the Capability. In this way the proposed graphical methods give a clear direction of quality improvement. We evaluate two different Process Capability plots, the (δ*, γ*)-plot and the confidence rectangle plot, from a theoretical as well as a practical point of view. When studying them from a theoretical point of view, among other things, a simulation study is conducted to investigate the ability of each of the two methods to identify that a Process is capable when it actually is. The comparison from a practical point of view is made by discussing the advantages and disadvantages of the two methods in different practical situations. Based on the above-mentioned comparisons, the recommendation is that the practitioner should use the (δ*, γ*)-plot. Copyright © 1999 John Wiley & Sons, Ltd.
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A pragmatic view on Process Capability studies
International Journal of Production Economics, 1999Co-Authors: Mats DelerydAbstract:In recent years an increasing number of organisations use Process Capability studies on a regular basis. Contemporaneous with the increasing number of organisations using Process Capability studies, warnings have been launched that imprudent use of numerical measures of Capability, the so-called Process Capability indices, might lead the user to make erroneous decisions. As a result, many practitioners of today are left with a somewhat ambivalent attitude towards Process Capability studies. In order to reduce these ambiguities, this paper outlines the advantages and disadvantages of the method. The results presented are based on a survey performed among 97 Swedish organisations that use Process Capability studies on a regular basis.
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Process Capability Studies for Short Production Runs
International Journal of Reliability Quality and Safety Engineering, 1998Co-Authors: Mats Deleryd, Kerstin VännmanAbstract:The current trend in modern production is directed towards shorter and shorter production runs. The two major reasons causing this trend are the rapid spread of the just in time (JIT) philosophy and the constantly increasing multiplicity of customer demands. The short runs of modern production not only constitute a challenge for production management, but they also cause some problems when applying traditional statistical methods, designed to be used for large sets of data. One of these methods is Process Capability studies. Since theories on how to use Process Capability studies in short production environments are incomplete, the aim of this paper is to present some ideas which will partly fill this gap. The theories of Process Capability studies for short runs presented are based on ideas of focusing on the Process, not on the products, and on using data transformation. By using the transformation presented, it is possible to conduct Process Capability studies in a traditional straightforward manner. A simulation study shows that the suggested transformation technique works satisfactorily in real situations. Finally, the -plot is introduced as a method of interpreting and analyzing the Capability of a short run production Process. By using the -plot, additional information is obtained concerning the Capability of a Process, compared to using traditional Process Capability indices only.
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Process Capability Studies for Short Production Runs
International Journal of Reliability Quality and Safety Engineering, 1998Co-Authors: Mats Deleryd, Kerstin VännmanAbstract:The current trend in modern production is directed towards shorter and shorter production runs. The two major reasons causing this trend are the rapid spread of the just in time (JIT) philosophy and the constantly increasing multiplicity of customer demands. The short runs of modern production not only constitute a challenge for production management, but they also cause some problems when applying traditional statistical methods, designed to be used for large sets of data. One of these methods is Process Capability studies. Since theories on how to use Process Capability studies in short production environments are incomplete, the aim of this paper is to present some ideas which will partly fill this gap. The theories of Process Capability studies for short runs presented are based on ideas of focusing on the Process, not on the products, and on using data transformation. By using the transformation presented, it is possible to conduct Process Capability studies in a traditional straightforward manner. A simulation study shows that the suggested transformation technique works satisfactorily in real situations. Finally, the [Formula: see text]-plot is introduced as a method of interpreting and analyzing the Capability of a short run production Process. By using the [Formula: see text]-plot, additional information is obtained concerning the Capability of a Process, compared to using traditional Process Capability indices only.