PROPUESTA DEL INDICE DE CAPACIDAD DPMO BASADO EN LA DESVIACIÓN MEDIA

El presente artículo compara los índices de capacidad de un mismo proceso, cuyos cálculos se realizaron utilizando la desviación estándar y la desviación media. El campo de interés está centrado en el índice DPMO y el nivel sigma de un proceso. Con base en el estudio realizado se ha podido concluir que, al hacer uso de la desviación media para encontrar el índice de capacidad de un proceso, conlleva a un proceso con un menor porcentaje de defectos

Indicadores multivariados de capacidad de procesos. Su eficiencia bajo distribuciones normales

n.d.Fil: Pagura, José Alberto - Facultad Ciencias Económicas y Estadística - Universidad Nacional de Rosario- Argentin

Propuesta para evaluar la capacidad de procesos de manufactura multivariados

Los productos manufacturados hoy en día poseen varias características de calidad que son importantes para el cliente y cuando no están correlacionadas, se usan varios índices de capacidad, tales como el Cp, Cpk y Cpm para evaluar la habilidad que tiene el proceso de producir productos de calidad. Para el caso contrario, se encontró en la revisión de literatura, índices de capacidad propuestos para medir la capacidad de un proceso cuando las características de calidad están correlacionadas. La mayoría de las propuestas coinciden en que debe definirse una región de especificación que represente lo que el cliente desea y otra región de variación del proceso que muestre el desempeño que tiene el proceso. Adicionalmente, como resultados de la revisión anterior, se observa que los autores difieren en la forma de definir ambas regiones, oportunidad que se aprovecha para presentar una nueva propuesta, mediante la cual se definen de una manera confiable ambas regiones y que al compararlas se obtienen un par de índices de capacidad multivariados CpM y CpkM, que son extensiones de los índices univariados Cp y Cpk. Por último, se utilizan los datos de un proceso, los cuales se pueden modelar por medio de una distribución normal bivariada para calcular los índices de capacidad propuestos en este trabajo, posteriormente se comparan con el valor de otros índices de capacidad similares, propuestos por otros autores, obteniéndose un  desempeño por sobre otros índices de la literatura consultada.The manufactured products have several quality characteristics that are important for the customer. When the quality characteristics are not correlated, using several indices, such as Cp, Cpk and Cpm to assess the ability of the process to produce quality products. For the opposite case was found in the literature review, proposed capability indices to measure the ability of a process when the quality characteristics are correlated. Most proposals agree that should define a specification region that represents what the customer wants, and another region of process variation that shows performance having the process. Additionally, as a result of the review, it was observed that different authors differ on how to define both regions, an opportunity that is used to present a new proposal, in which these regions are defined in a reliable manner and are compared, resulting in a pair of multivariate capability indices CpM and CpkM, which are extensions of univariate indexes Cp and Cpk. Finally, data from a process, which can be modeled by means of a bivariate normal distribution are used to calculate the capability indices proposed in this paper, then compared with the value of other similar capability indices, proposed by others authors give a performance over other indices of the literature

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

In the multivariate analysis of capacity there are many aspects, not even resolved yet, around some indexes such as the non-normality of the data and if the variables of quality are correlated or not. In this work we intend to propose the development of a multivariate capacity index (ICPM, for its acronym in Spanish) that works for both previous cases through simulated data examples. It is worth mentioning that we find a wide performance of the proposed index in comparison with other similar capacity indexes.En el análisis de capacidad multivariado existen muchos aspectos aún no resueltos en torno a algunos índices, como la no normalidad de los datos y si las variables de calidad están correlacionadas o no están correlacionadas. En este trabajo se pretendió proponer la elaboración de un índice de capacidad multivariado (ICPM) que funcione para ambos casos anteriores por medio de ejemplos de datos simulados. Cabe mencionar que se encontró un amplio desempeño del índice propuesto frente a otros índices de capacidad similares

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

DOI: https://doi.org/10.26439/ing.ind2020.n038.4814 The multivariate process capability analysis includes many indices that are only used when the data is normal and others, when the data is not normal. The same occurs with correlated and uncorrelated quality variables. In this research work, a CPME multivariate capability index was developed by initially using a univariate index—depending on whether the data was o not normal—and any correlation between variables, and then through a characteristic function for the multivariate data. This index may be used in all the aforementioned cases. Some examples of this alternative are presented on a set of real and simulated data, where a broad performance of our proposed index was found against other similar capability indices.DOI: https://doi.org/10.26439/ing.ind2020.n038.4814 En el análisis de capacidad de procesos multivariados existen muchos índices que solo se aplican cuando los datos son normales y otros cuando los datos son no normales; lo mismo ocurre cuando las variables de calidad están correlacionadas y no correlacionadas. En este trabajo se propone un índice de capacidad multivariado CPME, desarrollado bajo el uso inicial de un índice univariado, según sea el caso normal o no normal, y cualquier correlación entre variables, para luego, a través de una función característica, extenderlo para el caso multivariado. Este índice puede ser aplicado para todos los casos anteriores. Como utilidad, presentamos ejemplos de aplicación de esta alternativa sobre un conjunto de datos reales y simulados, donde se encontró un amplio desempeño del índice propuesto frente a otros de capacidad similares

Tolerance analysis in manufacturing using process capability ratio with measurement uncertainty

Tolerance analysis provides valuable information regarding performance of manufacturing process. It allows determining the maximum possible variation of a quality feature in production. Previous researches have focused on application of tolerance analysis to the design of mechanical assemblies. In this paper, a new statistical analysis was applied to manufactured products to assess achieved tolerances when the process is known while using capability ratio and expanded uncertainty. The analysis has benefits for process planning, determining actual precision limits, process optimization, troubleshoot malfunctioning existing part. The capability measure is based on a number of measurements performed on part’s quality variable. Since the ratio relies on measurements, elimination of any possible error has notable negative impact on results. Therefore, measurement uncertainty was used in combination with process capability ratio to determine conformity and nonconformity to requirements for quality characteristic of a population of workpieces. A case study of sheared billets was described where proposed technique was implemented. The use of ratio was addressed to draw conclusions about non-conforming billet’s weight expressed in parts per million (ppm) associated with measurement uncertainty and tolerance limits. The results showed significant reduction of conformance zone due to the measurement uncertainty

Monitoring, diagnostics and improvement of process performance

The data generated in a chemical industry is a reflection of the process. With the modern computer control systems and data logging facilities, there is an increasing ability to collect large amounts of data. As there are many underlying aspects of the process in that data, with its proper utilization, it is possible to obtain useful information for process monitoring and fault diagnosis in addition to many other decision making activities. The purpose of this research is to utilize the data driven multivariate techniques of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) for the estimation of process parameters. This research also includes analysis and comparison of these techniques for fault detection and diagnosis along with introduction, explanation and results from a new methodology developed in this research work namely Hybrid Independent Component Analysis (HICA).The first part of this research is the utilization of models of PCA and ICA for estimation of process parameters. The individual techniques of PCA and ICA are applied separately to the original data set of a waste water treatment plant (WWTP) and the process parameters for the unknown conditions of the process are calculated. For each of the techniques (PCA and ICA), the validation of the calculated parameters is carried out by construction of Decision Trees on WWTP dataset using inductive data mining and See 5.0. Both individual techniques were able to estimate all parameters successfully. The minor limitation in the validation of all results may be due to the strict application of these techniques to Gaussian and non-Gaussian data sets respectively. Using statistical analysis it was shown that the data set used in this work exhibits Gaussian and non-Gaussian behaviour.In the second part of this work multivariate techniques of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been used for fault detection and diagnosis of a process along with introduction of the new technique, Hybrid Independent Component Analysis (HICA). The techniques are applied to two case studies, the waste water treatment plant (WWTP) and an Air pollution data set. As reported in literature, PCA and ICA proved to be useful tools for process monitoring on both data set, but a comparison of PCA and ICA along with the newly developed technique (HICA) illustrated the superiority of HICA over PCA and ICA. It is evident from the fact that PCA detected 74% and 67% of the faults in the WWTP data and Air pollution data set respectively. ICA successfully detected 61.3% and 62% of the faults from these datasets. Finally HICA showed improved results by the detection of 90% and 81% of the faults in both case studies. This showed that the new developed algorithm is more effective than the individual techniques of PCA and ICA. For fault diagnosis using PCA, ICA and HICA, contribution plots are constructed leading to the identification of responsible variable/s for a particular fault. This part also includes the work done for the estimation of process parameters using HICA technique as was done with PCA and ICA in the first part of the research. As expected HICA technique was more successful in estimation of parameters than PCA and ICA in line with its working for process monitoring

Controle estatístico do processo de torneamento duro na presença de variável de ruído

The hard turning process has characteristics that favor its choice in relation to traditional turning. It presents better results of surface integrity, short process time, possibility of working without the use of lubricating fluid. Surface integrity results are the focus of studies of machining processes in general. The surface roughness is observed in relation to several controllable input parameters. Among the inputs can also be identified noise variables. These variables are uncontroled, but they affect process responses. In addition, there is more than one roughness response. Multivariate studies allow the simultaneous analysis of correlated responses. Thus, this research aims to perform univariate and multivariate analysis by principal components of the hard turning process. This analysis seeks to observe the effect of the noise variable on the responses and verify process stability and capability. For such analysis a hybrid method of experiment design and statistical process control was used. The AISI 52100 hardened steel turning was selected for the study application. Process scenarios were developed with a factorial arrangement. For this scenarios, regression models, control charts, process performance, sigma level and parts per million estimation were calculated. The performance (Ppk) results for the multivariate scenario were between 0.18 to 1.11. The best scenario presented had a cutting speed of 170m/min and a flow of lubricating fluid of 3 l/min.Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPqO processo de torneamento duro do aço ABNT 52100 tem características que favorecem sua seleção por apresentar resultados satisfatórios de integridade da superfície, menores tempo de processo, possibilidade de usinagem sem uso de fluido refrigerante. Pesquisas indicam que resultados de integridade da superfície são foco de estudos de processos por torneamento em geral. As rugosidades superficiais são observadas em relação a diversos parâmetros controláveis de entrada. Dentre as entradas pode se identificar também variáveis de ruído. Essas variáveis não estão sob controle, mas afetam as respostas do processo. Além disso, podem existir mais de uma resposta de rugosidade. Para isso estudos multivariados permitem a análise simultânea das respostas correlacionadas. Desse modo, essa pesquisa objetiva realizar análises univariada e multivariada por meio de componentes principais do processo de torneamento duro. Essa análise busca observar o efeito da variável de ruído nas respostas e verificar a estabilidade e desempenho do processo. Para tal análise um método híbrido de projeto de experimentos e controle estatístico do processo o torneamento do aço ABNT 52100 foi selecionado para aplicação do estudo. Com um arranjo fatorial foram desenvolvidos e comparados cenários do processo. Para esses cenários foram calculados modelos de regressão, cartas controle, performance do processo, estimativa de nível sigma e defeitos por milhão. Os resultados de performance (Ppk) para o cenário multivariado foram de 0.18 a 1.11. O melhor cenário apresentado teve velocidade de corte 170m/min e vazão de fluido refrigerante de 3l/min

Análise de Capabilidade de Processos Multivariados usando o Método dos Componentes Principais Ponderados.

A análise de capabilidade de processos tem sido usada para quantificar quão bem o processo atende aos requisitos dos clientes e para identificar e reduzir a variabilidade. No entanto, existe uma lacuna para se quantificar quando o processo apresenta características de qualidade correlacionadas, situação comum nos processos de fabricação. Considerando que os estudos desenvolvidos para dita análise em relação às métricas e propriedades estatísticas são em sua maioria para processos univariados, e que os métodos multivariados existentes apresentam restrições que fazem que sua aplicação seja limitada. Portanto, a principal contribuição deste estudo é a proposta de um método para análise multivariada de capabilidade, denominado Componentes Principais Ponderados (WPC). Este utiliza como resposta do modelo os escores dos componentes principais, ponderados por seus autovalores ou pela porcentagem de explicação de cada componente. O método proposto por Liao (2005) tem sido usado na otimização de processos com múltiplas respostas e foi aplicado por Peruchi et al. (2013) na determinação de índices de avaliação de sistemas de medição. No que diz respeito a esta nova abordagem, o WPC não foi simplesmente aplicado para se determinar os índices multivariados dos estimadores clássicos de capabilidade Cp, Cpk, Cpm e Cpmk; mas também para se estimar os índices de desempenho Pp, Ppk, Ppm e Ppmk, os intervalos de confiança das estimativas, a proporção de não conformes em PPM e o nível sigma. A eficiência do método se demonstrou usando dados da literatura, experimentais e simulados; neste último caso foi testado em diferentes níveis de desempenho de processo e graus de correlação. Em todos esses casos as estimativas do WPC foram comparadas com as de outros três métodos baseados em Análise de Componentes Principais (PCAM, PCAX e PCAW), e julgadas com intervalos de confiança univariados para se determinar se eram adequadas. O WPC se mostrou mais robusto do que os métodos PCAM e PCAW; além disso, exibiu um decrescimento das estimativas de desempenho com o aumento da correlação, tendência que já tem sido comprovada pelas pesquisas de Tano e Vännman (2013) e Guevara e Vargas (2007), e que é oposta aos métodos apresentados baseados em PCA. Isto faz presumir que o método proposto representa melhor o efeito de correlação entre as características, embora os métodos PCAX e PCAM tenham um bom comportamento perante dos intervalos de confiança univariados

Multivariate process capability using principal component analysis in the presence of measurement errors

Multivariate process capability indices, Principal components analysis, Measurement errors, Gauge Study, MANOVA,