Process-Oriented Basis Representations for Multivariate Process Diagnosis and Control

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9700191 Gonzalez-Barreto Advances in the sophistication of sensoring devices have led to production systems that simultaneously monitor many product quality characteristics. The availability of these data provide a great opportunity for process monitoring and control. This research, a collaboration between the Pennsylvania State University and the University of Puerto Rico, investigates ways to link patterns in multivariate quality data with patterns associated with certain kinds of production problems or process parameters. The researchers will develop new statistical process control tools to assist quality engineers in directly diagnosing the likely causes of production irregularities. Their methodology uses a process-oriented basis approach, whereby quality measurement vectors are correlated with hypothesized problems in the manufacturing process. For a given measurement vector, the methodology will identify a small set of potential causes, i.e. those basis elements with large coefficients. Process engineers then select appropriate control actions in either a manual or automated fashion. All manufacturing industries recognize that quality monitoring and control are critical elements in enabling fast and efficient production. As production processes become more sophisticated, it becomes increasingly important not only to monitor product quality, but also to quickly and accurately adjust process parameters when quality metrics degrade, This research addresses fundamental statistical problems in multivariate quality control and process rectification, and as such is expected to have considerable impact on the ability of engineers to accurately diagnose production problems caused by process variables. The investigators will work closely with three manufacturers of electronic components and/or systems in the conduct of this research, thus ensuring that the research is relevant to industry needs.