Enabling Cloud-Based Quality-Data Management Systems

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

Cloud-based platforms for accessing, sharing, and visualizing manufacturing-enterprise-level data are becoming available. In a cloud-based quality-data-management system, the quality-characteristics of different devices, products, and facilities are accumulated in a centralized database. These data pertain to multiple machines and multiple facilities, offering opportunities to achieve more effective quality control and productivity improvements. However, most cloud-based platforms are as yet unable to exploit the information contained in such data to make better decisions for production-system control and quality improvement. The objective of this project is to advance a series of methodologies that enable modeling of a large number of quality characteristics, timely change detection, accurate root cause diagnosis, and optimal repair decision-making. The project will also contribute to workforce training by offering students opportunities to engage in interdisciplinary research dealing with manufacturing, computing, sensing, and machine learning. The reason why cloud-based platforms may not as yet exploit manufacturing-enterprise-level data lies in the dearth of techniques to (1) describe the quality characteristics and their relationships, and (2) make decisions informed by such descriptive models. To enable cloud-based quality-data-management systems of the future, the investigators will first advance methodology needed for a flexible, yet rigorous, hierarchical graphical model, which will describe the inter-relationships among different quality characteristics. The hierarchical structure of the model will enable information sharing across different facilities within an enterprise. Based on this descriptive model, the investigators will next develop methodologies for process monitoring and diagnosis via likelihood based risk-adjustment and Bayesian-factor theory, and for optimal repair decisions via Partially Observable Markov Decision Processes (POMDP) framework. The developed methodologies will be tested on data obtained from an industrial collaborator.