Host Institution: |
La Trobe University |
Title of Seminar: |
Multivariate Process Capability Analysis |
Speaker's Name: |
Dr. Mali Abdollahian |
Speaker's Institution: |
RMIT University |
Time and Date: |
Friday 16 April 2010 at 11:00 am AEST |
Seminar Abstract: |
In real world quality characteristics describing a product are often interrelated with each other and do not follow a normal distribution. This non-normality and correlated characteristics of multivariate data poses a challenge to researchers to investigate accurate and effective process performance yardstick in the area of quality control. Multivariate capability measures that are currently employed, except for a handful of cases, depend intrinsically on the underlying multivariate data being normally distributed. We will present different methods to investigate a suitable multivariate performance measure. In the first section we will deploy geometric distance introduced by Wang (Wang 2006) to reduce the dimensionality of the correlated non-normal multivariate data and then fit Burr distribution to the geometric distance variable. The optimal parameters of the fitted Burr distribution will be estimated using different numerical techniques. The proportion of non-conformance (PNC) will be used as a criterion for process performance measurements. We will introduce an innovative approach for a multivariate capability index based on the Generalized Covariance Distance (GCD). This proposed approach is easy to use by frontline managers and quality practitioners. Another novelty introduced in this methodology is to approximate the distribution of these distances by a Burr XII distribution and then estimate its parameters using different numerical techniques. Examples based on real manufacturing process data are also presented which demonstrate that the proportion of nonconformance using proposed GCD method is very close to the actual proportion of nonconformance value. |
Seminar Convenor: |
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AGR IT support: |
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