Citation
Saupi, Ahmad Azizi
(2020)
Outliers identification and robust estimation method in analysis of crossed gage repeatability and reproducibility, random effect model.
Masters thesis, Universiti Putra Malaysia.
Abstract
Measurement system analysis (MSA) is a rigorous assessment of measurement
systems that is crucial in a manufacturing process. Under MSA, measurement
accuracy and precision are observed. Measurement accuracy comprises of
biasness and linearity of a gage while measurement precision focus on two types
of variations which are part to part variation and gage variation. Part variation is
the variability due to different items or units being measured while gage variation
is due to measurement systems that consists of repeatability and reproducibility
(R&R). This thesis will focus on crossed Gage R&R, random effect model.
Repeatability is the variation in the measurement system which is due to
measurement device while reproducibility is the variation in the measurement
system which is caused by differences between operators who record the
measurements. The Gage R&R study employs Analysis of Variance (ANOVA)
technique to analyse the variation associated with each component,
subsequently further analysis is done to determine whether or not measurement
system precision can be acceptable.
Nonetheless, many are not aware that outliers have an adverse effect on the
measurement system accuracy and precision. Hence, the effect of outliers on
the measurement system analysis, crossed Gage R&R random effect model is
analysed. The results clearly show that the measurement system accuracy and
precision are badly affected by outliers which provide misleading results to the
Gage R&R analysis and estimation. Therefore, the existence of outliers must be
identified and rectified to provide an accurate analysis of Gage R&R.
The classical standardized residual (CSR) is the widely used method to identify
outliers in the analysis of variance model. However, this method is not very successful in detecting outliers since the CSR is based on sample mean in its
computation of residuals and mean squares errors. Hence, robust standardized
residual (RSR) is formulated by incorporating median in the calculation of
residuals and mean squares errors. The findings show that the RSR is very
successful in identifying outliers with no masking effect and smaller rate of
swamping.
This thesis also addresses the problems of parameters estimation in the crossed
Gage R&R, random effect model when outliers are present in a data set. The
classical analysis of variance (ANOVA) method is the commonly used method
to estimate the parameters of the model. The shortcoming of using the classical
ANOVA is that the computation of each source of variation is based on sample
mean which is easily affected by outliers. In order to give an accurate and
efficient estimation, the effect of outliers should be reduced. Thus, an improvised
ANOVA is developed by incorporating a weighting scheme such that outlying
observations will be assigned a weight less than 1 while good observations are
set a value equal to 1. The results of the study signify that the improvised ANOVA
is more efficient than the classical ANOVA.
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