An analysis of Taguchi's on‐line quality monitoring procedure for attributes with diagnosis errors

Borges, Wagner de Souza; Ho, Linda Lee; Turnes, Osiris

doi:10.1002/asmb.442

Cited by 20 publications

(29 citation statements)

References 8 publications

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“…That is, the process is in-control at the i-th inspection, and it also stays in-control at the (i + 1)-th inspection. The non-null elements of the matrix A 00 are expressed in (2).…”

Section: An Analysis Of Taguchi's On-line Quality Monitoring Procedurmentioning

confidence: 99%

An analysis of Taguchi's on-line quality monitoring procedure for variables based on the results of a sequence of inspections

Quinino

2012

Pesqui. Oper.

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ABSTRACT. In this paper, a procedure for the on-line process control of variables is proposed. This procedure consists of inspecting the m-th item from every m produced items and deciding, at each inspection, whether the process is out-of-control. Two sets of limits, warning (μ 0 ± W ) and control (μ 0 ± C), are used. If the value of the monitored statistic falls beyond the control limits or if a sequence of h observations falls between the warning limits and the control limits, the production is stopped for adjustment; otherwise, production goes on. The properties of an ergodic Markov chain are used to obtain an expression for the average cost per item. The parameters (the sampling interval m, the widths of the warning, the control limits W and C(W ≤ C), and the sequence length (h) are optimized by minimizing the cost function. A numerical example illustrates the proposed procedure.

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“…That is, the process is in-control at the i-th inspection, and it also stays in-control at the (i + 1)-th inspection. The non-null elements of the matrix A 00 are expressed in (2).…”

Section: An Analysis Of Taguchi's On-line Quality Monitoring Procedurmentioning

confidence: 99%

An analysis of Taguchi's on-line quality monitoring procedure for variables based on the results of a sequence of inspections

Quinino

2012

Pesqui. Oper.

Self Cite

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“…Although in the development of the Bayesian estimation methodology, we have assumed that the inspection error is negligible, this may not be the case in all situations. Borges, Ho, and Turnes (2001) developed an extension of the problem in the presence of diagnostic errors. As they showed, extension of the results of Nayebpour and Woodall (1993) requires the development of a new model.…”

Section: Effect Of Sample Size (N)mentioning

confidence: 99%

“…The optimal monitoring procedures are more sensitive to changes in type I error than to changes in type II error. But Borges et al (2001) assumed that p and π are known. Here we show how the presence of type I and type II inspection errors affect the estimation of p and π and thus the optimal monitoring procedure.…”

Section: Effect Of Sample Size (N)mentioning

confidence: 99%

Estimation of Process Parameters to Determine the Optimum Diagnosis Interval for Control of Defective Items

Dasgupta

Mandal

2008

Technometrics

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The online quality monitoring procedure for attributes proposed by Taguchi has been critically studied and extended by a few researchers. Determination of the optimum diagnosis interval requires estimation of some parameters related to the process failure mechanism. Improper estimates of these parameters may lead to an incorrect choice of the diagnosis interval and thus huge economic penalties. We propose a Bayesian approach to estimate the process parameters under two different process models, commonly called as the case II and case III models in the literature. We discuss a systematic way to use available engineering knowledge in eliciting the prior for the parameters, and demonstrate the performance of the proposed method using extensive simulation and a case study from a hot rolling mill.

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“…As probabilidades restantes, P (1,1), (2,1) , P (1,1),(2,0) , P (2,1),(2,1) e P (2,1), (2,0) , devem considerar apenas a probabilidade de julgar o item inspecionado conforme ou não conforme, pois o ciclo já se inicia no estado de produção II. (1 ) P P y P P − = + .…”

Section: Modelo Probabilísticounclassified

“…No modelo de controle para atributos apresentado por Borges et al (2001) admite-se que o sistema de inspeção está sujeito a erros de classificação. Pode-se julgar um item conforme dado que ele não o é (erro tipo II) ou julgar um item conforme como não conforme (erro tipo I).…”

Section: Introductionunclassified

Controle on-line por atributos com erros de classificação: uma abordagem econômica com classificações repetidas

Trindade

Quinino

2007

Pesqui. Oper.

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ResumoO procedimento de controle on-line de processos por atributos, proposto por Taguchi et al. (1989), consiste em amostrar um item a cada m produzidos e decidir, a cada inspeção, se houve ou não a redução da fração de itens conformes produzidos. Caso o item inspecionado for não conforme, pára-se o processo para ajuste. Como o sistema de inspeção pode estar sujeito a erros de classificação, desenvolve-se um modelo probabilístico que considera classificações repetidas e independentes do item amostrado em um sistema de controle com inspeção imperfeita. Utilizando-se as propriedades de uma cadeia de Markov ergódica, obtém-se uma expressão do custo médio do sistema de controle, que pode ser minimizada por três parâmetros: o intervalo entre inspeções; o número de classificações repetidas; e o número mínimo de classificações conformes (dentre as classificações repetidas), para julgar um item como conforme. Um exemplo numérico ilustra o procedimento proposto.Palavras-chave: controle on-line de qualidade por atributos; erros de classificação; cadeias de Markov. AbstractThe procedure to control on-line processes for attributes proposed by Taguchi et al. (1989) consists of sampling a single item at very m produced ones. If the examined item is non-conforming, the process is said to be out of control (the fraction of conforming item is reduced) and it is stopped for adjustment. However, the inspection procedure is subject to misclassification errors. So a probabilistic model was developed considering results of the repetitive and independent classifications of the examined item. Employing properties of an ergodic Markov chain, an expression of the expected cost function for the control system was obtained to be minimized by three parameters: sampling interval; number of the repetitive classifications; minor number of conforming classifications (among the repetitive classifications) to declare an item as conforming. A numerical example illustrates the proposed model.

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An analysis of Taguchi's on‐line quality monitoring procedure for attributes with diagnosis errors

Cited by 20 publications

References 8 publications

An analysis of Taguchi's on-line quality monitoring procedure for variables based on the results of a sequence of inspections

An analysis of Taguchi's on-line quality monitoring procedure for variables based on the results of a sequence of inspections

Estimation of Process Parameters to Determine the Optimum Diagnosis Interval for Control of Defective Items

Controle on-line por atributos com erros de classificação: uma abordagem econômica com classificações repetidas

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