32 research outputs found

    Computing Multivariate Process Capability Indices With Microsoft Excel

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    In manufacturing industry there is growing interest in measures of process capability under multivariate setting. While there are many statistical packages to assess univariate capability, a current problem with the multivariate measures of capability is the shortage of user friendly software. In this paper a Visual Basic program has been developed to realize an Excel spreadsheet that may be used to compute two multivariate measures of capability. Our aim is to provide a useful tool for practitioners dealing with multivariate capability assessment problems. The features of the program include easy data entry and clear report formatMultivariate Process capability indices, statistical quality control, Visual Basic, Excel Indici di capacità multivariati, Controllo statistico della qualità

    MPCI : An R Package for Computing Multivariate Process Capability Indices

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    Manufacturing processes are often based on more than one quality characteristic. When these variables are correlated the process capability analysis should be performed using multivariate statistical methodologies. Although there is a growing interest in methods for evaluating the capability of multivariate processes, little attention has been given to developing user friendly software for supporting multivariate capability analysis. In this work we introduce the package MPCI for R, which allows to compute multivariateprocess capability indices. MPCI aims to provide a useful tool for dealing with multivariate capability assessment problems. We illustrate the use of MPCI package through both simulated and real examples

    Computing Multivariate Process Capability Indices (Excel)

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    In manufacturing industry there is growing interest in measures of process capability under multivariate setting. Although there are many statistical packages to assess univariate capability, a current problem with the multivariate measures of capability is the shortage of user friendly software. In this article a Visual Basic program has been developed to realize an Excel spreadsheet that may be used to compute two multivariate measures of capability. The aim of this article is to provide a useful tool for practitioners dealing with multivariate capability assessment problems. The features of the program include easy data entry and clear report format

    Computing Multivariate Process Capability Indices With Microsoft Excel

    Get PDF
    In manufacturing industry there is growing interest in measures of process capability under multivariate setting. While there are many statistical packages to assess univariate capability, a current problem with the multivariate measures of capability is the shortage of user friendly software. In this paper a Visual Basic program has been developed to realize an Excel spreadsheet that may be used to compute two multivariate measures of capability. Our aim is to provide a useful tool for practitioners dealing with multivariate capability assessment problems. The features of the program include easy data entry and clear report format

    APPLICATION OF PROCESS CAPABILITY INDICES TO MEASURE PERFORMANCE OF A MULTISTAGE MANUFACTURING PROCESS

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    Abstract A process is a unique combination of manpower, machines, methods and materials in providing a product or service. Process capability indices have been used in the manufacturing industry to provide quantitative measures of process potential and performance. High quality production provides advantages such as cost saving, reduced scrap or remanufacturing, higher yield and increased customer satisfaction and market share. Process capability indices (PCI) are extensively used in industry to evaluate the conformation of products (process yield) to their specifications. Conventional univariate process capability indices such as Cp and Cpk are applied to measure performance for single quality characteristic. In modern manufacturing when product designs are complicated and consumer's requirements are changeable day to day, multiple quality characteristics must be simultaneously evaluated to improve product's quality and also to consider correlations exist among the quality characteristics. In this paper process capability indices (both univariate and multivariate) are applied to measure performance in a multistage 'locomotive wheel' manufacturing process. The wheel manufacturing process has three stages namely press forging, rolling and heat treatment. Process capability indices are analysed for the above mentioned multistage manufacturing processes and the results are compared to identify the most accurate multivariate process capability index to evaluate multiple quality characteristics for the wheel manufacturing. The results show multivariate process capability indices (MVPCI) proposed b

    Un índice de capacidad de procesos para distribuciones multivariadas normales de variables correlacionadas y no correlacionadas

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    In the multivariate analysis of capacity many aspects even not exist resolved around some indexes and their application. For example, when the variables of quality are correlated and when they are not correlated. In this work we seek to propose the construction of a multivariate index of capacity that works for both previous cases, and to present an example of application of this alternative on a real dataset. A wide acting of our index is obtained proposed in front of other similar indexes of capacity.En el análisis de capacidad multivariado existen muchos aspectos aún no resueltos en torno a algunos índices y su aplicación. Por ejemplo, cuando las variables de calidad están correlacionadas y cuando no están correlacionadas. En este trabajo pretendemos proponer la construcción de un índice de capacidad multivariado que funcione para los dos casos anteriores, y presentar un ejemplo de aplicación de esta alternativa sobre un conjunto de datos reales. Nuestro índice obtuvo un amplio desempeño frente a otros índices de capacidad similares.   &nbsp

    Comparación del desempeño de índices de capacidad multivariados a través de la simulación de procesos

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    En el presente trabajo se evaluará el desempeño de índices multivariados en procesos simulados con cambios en su distribución y parámetros (centramiento, variabilidad y correlación), los niveles de correlación son Nula, Débil, Media y Fuerte. Los índices evaluados son el Método ECPK propuesto por Braun (2001), el Índice MCPM propuesto por Taam, Subbarah y Liddy (1993), los Índices MCP y MCPC propuesto por Wang y Du (2000), el Vector de Capacidad de Procesos Multivariados (VCPM) propuesto por Shahriari, Hubele and Lawrence (1995) y el Nuevo Vector de Capacidad de Procesos Multivariados (NVCPM) propuesto por Shariahri y Abdollahzadeh (2009). Esta investigación se enfocará en realizar un Análisis de la Capacidad de Procesos Multivariantes utilizando los índices de Capacidad de Procesos Multivariados, los cuales ayudarán a indicar cuándo un proceso tiene la habilidad de cumplir con las características y especificaciones que se han establecido previamente, esto con el fin de comparar el comportamiento de dichos índices y finalmente, definir cuál es el mejor índice para cada escenario planteado. La metodología estadística utilizada se basa en la simulación de procesos multivariados, que tienen diferentes características con respecto al centramiento, variabilidad y niveles de correlación. A estos se les realiza un Análisis de Capacidad Multivariado con los Índices de Capacidad propuestos, los cuales cuantificarán la habilidad que tienen dichos procesos para cumplir las especificaciones determinadas por los clientes o el mercado. Previamente, los procesos generados se identifican como procesos capaces o no capaces por medio de una regla fundamental de Calidad (Seis Sigma) que determina que sólo hasta 0, 027 % de los datos de un proceso estarán por fuera de los límites de especificación que se han generado para 2 y 3 variables. Por último, se realiza una comparación del desempeño de cada uno de los índices y se determinará el mejor para cada uno de los procesos simulados por medio de un indicador llamado Porcentaje de Diagnóstico de Capacidad ( %DC), que ayudará a juzgar si los índices son lo suficientemente buenos como para ser utilizados con cierta seguridad por parte de los Coordinadores de Calidad en las industriasPregradoESTADISTICO(A

    Development and application of process capability indices

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    In order to measure the performance of manufacturing processes, several process capability indices have been proposed. A process capability index (PCI) is a unitless number used to measure the ability of a process to continuously produce products that meet customer specifications. These indices have since helped practitioners understand and improve their production systems, but no single index can fully measure the performance of any observed process. Each index has its own drawbacks which can be complemented by using others. Advantages of commonly used indices in assessing different aspects of process performance have been highlighted. Quality cost is also a function of shift in mean, shift in variance and shift in yield. A hybrid is developed that complements the strengths of these individual indices and provides the set containing the smallest number of indices that gives the practitioner detailed information on the shift in mean or variance, the location of mean, yield and potential capability. It is validated that while no single index can fully assess and measure the performance of a univariate normal process, the optimal set of indices selected by the proposed hybrid can simultaneously provide precise information on the shift in mean or variance, the location of mean, yield and potential capability. A simulation study increased the process variability by 100% and then reduced by 50%. The optimal set managed to pick such a shift. The asymmetric ratio was able to detect both the 10% decrease and 20% increase in µ but did not alter significantly with a 50% decrease or a 100% increase in σ, which meant it was not sensitive to any shift in σ. The implementation of the hybrid provides the quality practitioner, or computer-aided manufacturing system, with a guideline on prioritised tasks needed to improve the process capability and reduce the cost of poor quality. The author extended the proposed hybrids to fully measure the performance of a process with multiple quality characteristics, which follow normal distribution and are correlated. Furthermore, for multivariate normal processes with correlated quality characteristics, process capability analysis is not complete without fault diagnostics. Fault diagnostics is the identification and ranking of quality characteristics responsible for multivariate process poor performance. Quality practitioners desire to identify and rank quality characteristics, responsible for poor performance, in order to prioritise resources for process quality improvement tasks thereby speeding up the process and minimising quality costs. To date, none of the existing commonly used source identification approaches can classify whether the process behaviour is caused by the shift in mean or change in variance. The author has proposed a source identification algorithm based on mean and variance impact factors to address this shortcoming. Furthermore, the author developed a novel fault diagnostic hybrid based on the proposed optimal set selection algorithm, principal component analysis, machine learning, and the proposed impact-factor. The novelty of this hybrid is that it can carry out a full multivariate process capability analysis and provides a robust tool to precisely identify and rank quality characteristics responsible for the shifts in mean, variance and yield. The fault diagnostic hybrid can guide the practitioners to identify and prioritise quality characteristics responsible for the poor process performance, thereby reducing the quality cost by effectively speeding up the multivariate process improvement tasks. Simulated scenarios have been generated to increase/decrease some components of the mean vector (µ2/µ4) and in increase/reduce the variability of some components (σ1 reduced to close to zero/σ6 multiplied by 100%). The hybrid ranked X2 and X6 as the most contributing variables to the process poor performance and X1 and X4 as the major contributors to process yield. There is a great challenge in carrying out process capability analysis and fault diagnostics on a high dimensional multivariate non-normal process, with multiple correlated quality characteristics, in a timely manner. The author has developed a multivariate non-normal fault diagnostic hybrid capable of assessing performance and perform fault diagnostics on multivariate non-normal processes. The proposed hybrid first utilizes the Geometric Distance (GD) approach, to reduce dimensionality of the correlated data into fewer number of independent GD variables which can be assessed using univariate process capability indices. This is followed by fitting Burr XII distribution to independent GD variables. The independent fitted distributions are used to estimate both yield and multivariate process capability in a time efficient way. Finally, machine learning approach, is deployed to carry out the task of fault diagnostic by identifying and ranking the correlated quality characteristics responsible for the poor performance of the least performing GD variable. The results show that the proposed hybrid is robust in estimating both yield and multivariate process capability, carrying out fault diagnostics beyond GD variables, and identifying the original characteristic responsible for poor performance. The novelty of the proposed non-normal fault diagnostic hybrid is that it considers quality characteristics related to the least performing GD variable, instead of investigating all the quality characteristics of the multivariate non-normal process. The efficacy of the proposed hybrid is assessed through a real manufacturing examples and simulated scenarios. Variables X1,, X2 and X3 shifted away from the target by 25%, 15% and 35%, respectively, and the hybrid was able to select variables X3 to be contributing the most to the corresponding geometric distance variable's poor performance

    Modeling and Optimization of Stochastic Process Parameters in Complex Engineering Systems

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    For quality engineering researchers and practitioners, a wide number of statistical tools and techniques are available for use in the manufacturing industry. The objective or goal in applying these tools has always been to improve or optimize a product or process in terms of efficiency, production cost, or product quality. While tremendous progress has been made in the design of quality optimization models, there remains a significant gap between existing research and the needs of the industrial community. Contemporary manufacturing processes are inherently more complex - they may involve multiple stages of production or require the assessment of multiple quality characteristics. New and emerging fields, such as nanoelectronics and molecular biometrics, demand increased degrees of precision and estimation, that which is not attainable with current tools and measures. And since most researchers will focus on a specific type of characteristic or a given set of conditions, there are many critical industrial processes for which models are not applicable. Thus, the objective of this research is to improve existing techniques by not only expanding their range of applicability, but also their ability to more realistically model a given process. Several quality models are proposed that seek greater precision in the estimation of the process parameters and the removal of assumptions that limit their breadth and scope. An extension is made to examine the effectiveness of these models in both non-standard conditions and in areas that have not been previously investigated. Upon the completion of an in-depth literature review, various quality models are proposed, and numerical examples are used to validate the use of these methodologies
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