This Sensors and Sensor Networks (NSF 05-526), Sensors Small Team (SST)/Grant Opportunity for Academic Research (GOALI)/Collaborative Research grant provides funding to establish, validate, and implement a comprehensive multi-sensor planning, integration, distribution, and decision-making methodology for effective dimensional quality control of complex manufacturing processes. The research will establish and integrate: (i) A new dimensional sensing system to provide spatially- and temporally-dense dimensional measurements of intermediate and final products. The basic approach is to integrate different coordinate measurement sensors with such characteristics as touch-probe point sensor with high accuracy but low speed; and area optical sensor with low accuracy but high speed; (ii) A math-based decision making methodology for effective root cause identification of process variation in complex manufacturing processes by integrating sensing data and a vast array of product and process design information; and (iii) A system-level optimal sensor distribution strategy for sensor distribution to achieve optimal diagnosability and inspectability for quality and productivity improvement. This project will be carried out in close collaboration with the University of Wisconsin - Madison, Illinois Institute of Technology and Dimensional Control System, Inc. The methodology development will be based on, and the resulting technology will be tested and implemented in the DCS process simulation software.
The multi-sensor planning, integration, and analysis techniques will link such varied areas as system theory, computer aided design, optimization, and advanced statistics to solve problems on manufacturing process control. As a result, a new sensor and multi-sensor network system will be developed to help manufacturers considerably reduce process variation while at the same time significantly improve productivity and quality. This technique, if successfully developed, will provide a substantial boost to the overall competitiveness of US industries. The project will also significantly contribute towards the development of new curriculum and educational efforts as it will provide multidisciplinary training for students in the areas of mechanical and industrial engineering, system science, and statistics. Research accomplishments will be transferred into undergraduate and graduate curricula and also result in laboratory development.
