This grant provides funding to develop new quality tools for manufacturing systems through incorporating computer and information science principles, such as those used in web mining. These newly envisioned quality methods will make use of data mining techniques such as association, clustering, and significance as a means of handling large amounts of process and product data being collected in current complex manufacturing systems in order to transform the data into usable knowledge. These new quality tools will be developed to store, organize, analyze, model, and visualize large, heterogeneous data sets associated with manufacturing systems for continuous quality and reliability quantification and improvement. These techniques will transform the current data-push quality approach, where reports are often generated without a clear purpose, into a data pull system where the quality system will reflect an awareness of the task environment while reacting to a fault (alarm condition) or a user query. This work includes a strong collaboration with QMC LLC, a data management company who will implement the developed tools, FARO, provider of dimensional data acquisition systems, and Ford Motor Company, who will provide a test bed to validate the proposed tools in an industrial environment.
The results of this research will redefine current quality control methods through transforming low-level data into high-content information leading to a deeper understanding of manufacturing and service systems. This will allow for quicker and more accurate failure detections, leading to a significant increase in quality. In addition, the ability to effectively handle data-rich manufacturing systems will stimulate the development and incorporation of advanced sensor and data collection techniques. This will produce a continuous quality improvement attitude based upon heterogeneous data management.
