CAREER: Highly-Efficient Dynamic Prediction Models for Quality Improvement in Cold Rolling

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This Faculty Early Career Development (CAREER) Program award supports fundamental research to lay the foundation to significantly improve the quality of aluminum, steel, copper, and brass sheets manufactured on cold rolling mills. These metal sheets are important raw materials for many products, including aircraft, automobiles, ships, refrigerators, computers, electric motors, and buildings. Rolled metal sheets of poor geometric quality need to undergo costly additional processing. Results from this research will lead to new mill control strategies that improve rolling quality and productivity for these metal sheets in important emerging markets. The research will benefit the economic competitiveness and security of the United States. The direct involvement of engineering students from the under-represented groups (including women and persons with autism) in manufacturing will provide positive benefits to education and society.Geometry defects in cold rolling of thin aluminum, steel, copper, and brass sheets arise from the mismatch of incoming strip thickness profiles with mill roll-bite profiles. Detailed roll-bite behavior stems from complex transient effects of mill structural deflection, thermal expansion, roll grinding, and roll wear, and is difficult to predict. The highly-efficient dynamic models to be created in this work will integrate structural, thermal, and wear patterns using a novel, mixed finite element approach to determine three-dimensional, high-frequency nonlinear dynamic responses of rolling mills. The models will allow for more efficient and accurate multi degree-of-freedom prediction of mill dynamics than is possible with single degree-of-freedom or full-scale finite element models. The efficient nonlinear, multi degree-of-freedom dynamic models will also be combined with Bayesian/Markov-Chain Monte-Carlo control approaches, to yield a better understanding of mill behavior in spite of significant random process variations. The understanding gained may lead to new on-mill roll grinding methods to correct geometry defects, novel two-dimensional (instead of centerline-only) gauge control, and probabilistic approaches to improve geometric quality of rolled metals.