Identifying important variation patterns is a key step to identifying root causes of process variability. This gives rise to a number of challenges. First, the variation patterns might be non-linear in the measured variables, while the existing research literature has focused on linear relationships. Second, it is important to remove noise from the dataset in order to visualize the true nature of the underlying patterns. Third, in addition to visualizing the pattern (preimage), it is also essential to understand the relevant features that define the process variation pattern.
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- Partial requirement for: Ph.D., Arizona State University, 2013Note typethesis
- Includes bibliographical references (p. 111-113)Note typebibliography
- Field of study: Industrial engineering