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    On the monitoring of multi-attributes high-quality production processes

    , Article Metrika ; Volume 66, Issue 3 , 2007 , Pages 373-388 ; 00261335 (ISSN) Akhavan Niaki, S. T ; Abbasi, B ; Sharif University of Technology
    2007
    Abstract
    Over the last decade, there have been an increasing interest in the techniques of process monitoring of high-quality processes. Based upon the cumulative counts of conforming (CCC) items, Geometric distribution is particularly useful in these cases. Nonetheless, in some processes the number of one or more types of defects on a nonconforming observation is also of great importance and must be monitored simultaneously. However, there usually exist some correlations between these two measures, which obligate the use of multi-attribute process monitoring. In the literature, by assuming independence between the two measures and for the cases in which there is only one type of defect in... 

    Skewness reduction approach in multi-attribute process monitoring

    , Article Communications in Statistics - Theory and Methods ; Volume 36, Issue 12 , 2007 , Pages 2313-2325 ; 03610926 (ISSN) Akhavan Niaki , S. T ; Abbasi, B ; Sharif University of Technology
    2007
    Abstract
    Since the product quality of many industrial processes depends upon more than one dependent variable or attribute, they are either multivariate or multi-attribute in nature. Although multivariate statistical process control is receiving increased attention in the literature, little work has been done to deal with multi-attribute processes. In this article, we develop a new methodology to monitor multi-attribute processes. To do this, first we transform multi-attribute data in a way that their marginal probability distributions have almost zero skewness. Then, we estimate the transformed covariance matrix and apply the well-known T2 control chart. In order to illustrate the proposed method... 

    Estimating process capability indices of multivariate nonnormal processes

    , Article International Journal of Advanced Manufacturing Technology ; Volume 50, Issue 5-8 , 2010 , Pages 823-830 ; 02683768 (ISSN) Abbasi, B ; Akhavan Niaki, S. T ; Sharif University of Technology
    Abstract
    The capability analysis of production processes where there are more than one correlated quality variables is a complicated task. The problem becomes even more difficult when these variables exhibit nonnormal characteristics. In this paper, a new methodology is proposed to estimate process capability indices (PCIs) of multivariate nonnormal processes. In the proposed methodology, the skewness of the marginal probability distributions of the variables is first diminished by a root transformation technique. Then, a Monte Carlo simulation method is employed to estimate the process proportion of nonconformities (PNC). Next, the relationship between PNC and PCI is found, and finally, PCI is...