There is much uncertainty and fuzziness in product quality attributes or quality parameters of a manufacturing process, so the traditional quality control chart can be difficult to apply. This paper proposes a fuzzy control chart. The plotted data was obtained by transforming expert scores into fuzzy numbers. Two types of nonconformity judgment rules—necessity and possibility measurement rules—...

  Multistage process monitoring has recently attracted notable attention in that the statistical relationships between quality variables are taken into account. Here, we dealt with the problem of monitoring and diagnosing a two-stage production process with attribute characteristics in which the outgoing quality variable is impacted by the incoming quality variable from the first process stage....

Control chart is the most well-known chart to monitor the number of nonconformities per inspection unit where each sample consists of constant size. Generally, the design of a control chart requires determination of sample size, sampling interval, and control limits width. Optimally selecting these parameters depends on several process parameters, which have been considered from statistical and...

The most well-known uni-arribute control chart used to monitor the number of nonconformities per unit is the Shewhart type C-chart. In this paper, a new method is proposed in an attempt to reduce the false alarm rate in the C-chart. To do this, the decision on beliefs (DOB) concept is first uti [1] Corresponding author e-mail: [email protected]   lized to design an iterative method, where...

A control chart is an important tool in statistical process monitoring that useful to monitor and improve production quality. In this article, attribute using repetitive sampling under a truncated life test proposed for the mean of product where lifetime follows Rayleigh distribution. The parameters limit coefficients are determined so in-control average run length (ARL) very close target ARL. ...

multivariate control charts such as hotelling`s t^ 2 and x^ 2 are commonly used for monitoring several related quality characteristics. these control charts use correlation structure that exists between quality characteristics in an attempt to improve monitoring. the purpose of this article is to discuss some issues related to the g chart proposed by levinson et al. [9] for detecting shifts in ...

This paper presents a control charting technique to monitor attribute data based on a generalized zero-inflated Poisson (GZIP) distribution, which is an extension of ZIP distribution. GZIP distribution is very flexible in modeling complicated behaviors of the data. Both the technique of fitting the GZIP model and the technique of designing control charts to monitor the attribute data based on t...

Monitoring multi-attribute processes is an important issue in many quality control environments. Almost all the priory proposed control charts utilize equal weights for each Attribute Quality Characteristics (AQCs). In such condition, there is no priority among AQCs. But in real-world, compensatory may exist. Hence due to some applied reasons such as function or efficiency, unequal weights for ...