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A novel variable selection approach based on co-linearity index to discover optimal process settings by analysing mixed data / C. Giannetti; R.S. Ransing; M.R. Ransing; D.C. Bould; D.T. Gethin; J. Sienz
Computers & Industrial Engineering, Volume: 72, Pages: 217 - 229
Swansea University Author: Giannetti, Cinzia
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In the last two decades the application of statistical techniques to process control has gained popularity due to the widespread adoption of quality management systems such as ISO9001:2008. In order to ensure that a process operates at its full potential and conforms to customers and legal requireme...
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In the last two decades the application of statistical techniques to process control has gained popularity due to the widespread adoption of quality management systems such as ISO9001:2008. In order to ensure that a process operates at its full potential and conforms to customers and legal requirements, Statistical Process Control (SPC) is often used by engineers to control process parameters and limit process variations. However traditional SPC techniques like the use of control charts and design of experiments have several limitations due to the fact that real industrial processes are often very complex and involve a large number of variables. In such cases it is unpractical to try to control all the variables independently. Furthermore these approaches would fail when the process output is influenced by the combined effect of several variables sometimes belonging to different sub-processes. Data heterogeneity is also another issue since process data often contain a mixture of categorical and continuous variables that need to be simultaneously analysed. In this paper a novel approach for discovering noise free correlations between heterogeneous process factors and responses is proposed. The method, based on the principles of Multiple Factor Analysis (MFA), allows presentation and visualisation of continuous and categorical variables in a single plot and can be used in real industrial settings to assist process engineers in manufacturing diagnosis and root cause analysis. The applicability and validity of this novel method has been demonstrated through two industrial case studies.
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