| Summary: | 碩士 === 國立成功大學 === 統計學系 === 102 === The multistage manufacturing processes with high-value-added products are become gradually important in today's industry thus the process analysis for quality-related problems of this kind of process draw more and more attention. In this thesis, we aim to provide a model building procedure for realizing the temporal influence of the process variables on final quality and for finding the root causes of abnormal quality products.
The automatic data collection tools within the process provide longitudinal measurement data of the process variables, these measurements are in the form of curve to which refer profile data. We consider the process profile data are of functional nature so that the techniques of functional data analysis can be applied. To relate the functional profile data to the final quality outcome, first we employed the functional linear model. For the functional linear model with multiple functional covariates, the least absolute shrinkage and selection operator (lasso) (Tibshirani, 1996) is introduced for simultaneous variable selection and parameter estimation.
The major contribution of this thesis is in developing a functional regression model which includes the temporal interaction between (and/or within) process profiles. A two-stage modeling approach is also proposed for keeping the main effect priority. Finally, the property of the proposed model is illustrated through a real data analysis. The result shows that the estimated models from the two-stage modeling approach are helpful for process analysis and root cause finding.
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