Necessary and sufficient condition for the stability of process variability
Nowadays complexity in industries are increasing, hence the need of tools to serve these complexities are inevitable. It is difficult to find a big scale industry that only monitoring one critical to quality (CTQ) parameter. In statistical process control (SPC) point of view the use of univ...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/85749/1/IPM%202020%202%20-%20ir.pdf |
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| Summary: | Nowadays complexity in industries are increasing, hence the need of tools to serve these
complexities are inevitable. It is difficult to find a big scale industry that only monitoring one
critical to quality (CTQ) parameter. In statistical process control (SPC) point of view the use
of univariate statistical process control (USPC) is no longer appropriate. Therefore, multivariate
statistical process control (MSPC) is the more appropriate tools to use.
Unfortunately, the MSPC tools available today are not reliable in terms of the appro- priateness,
the desired probability false alarm, and the in-controlled process. All the tools available for
monitoring process control is based on two major measures, gen- eralize variance (GV) and vector
variance (VV) which later will be shown to be not reliable as they only provide the necessary
chart, not necessary and sufficient chart. This thesis will overcome those problems by proposing a
new reliable necessary and sufficient chart based on two distance measures, Mahalanobis
distance-based and Euclidean distance-based.
In this thesis we will construct the multivariate process variability (MPV) monitoring based on
distance measure with its cut-off values. It will cover both the theoretical and simulation
aspects. Control chart will also be provided for the new proposed methods for both constant
sub-group size and general sub-group size. Industrial ex- amples will also be provided for the sake
of comparison with the currently available tools. Lastly, the root causes analysis will be carried
out. It is an analysis to identify the cause of out of control (OOC) signal. Industrial examples
will also be provided for root causes analysis.
The newly proposed MPV monitoring tools are considered very good as they can solve the reliability
problems by providing the necessary and sufficient chart and able to detect the OOC signal from the
simulation studies and industrial examples
provided. |
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