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

该学院早期职业发展（Career）计划奖支持基础研究，为显著提高冷轧机上生产的铝、钢、铜和黄铜板的质量奠定基础。这些金属板材是许多产品的重要原材料，包括飞机、汽车、船舶、冰箱、计算机、电动机和建筑物。轧制的金属板材几何质量差，需要进行昂贵的附加加工。这项研究的结果将导致新的轧机控制策略，提高这些金属板在重要的新兴市场的轧制质量和生产率。这项研究将有利于美国的经济竞争力和安全。来自弱势群体（包括妇女和自闭症患者）的工科学生直接参与制造业，将为教育和社会带来积极的好处。冷轧薄铝、钢、铜和黄铜板的几何缺陷是由于来料带材厚度轮廓与轧机轧辊咬合轮廓不匹配造成的。轧辊咬伤的详细行为是由轧机结构挠曲、热膨胀、轧辊磨削和轧辊磨损等复杂的瞬态效应引起的，难以预测。在这项工作中创建的高效动态模型将使用一种新颖的混合有限元方法集成结构，热和磨损模式，以确定轧机的三维，高频非线性动态响应。该模型将允许比单自由度或全尺寸有限元模型更有效和准确的多自由度轧机动力学预测。高效的非线性、多自由度动态模型也将与贝叶斯/马尔可夫链蒙特卡罗控制方法相结合，以便更好地理解尽管存在显著的随机过程变化的轧机行为。所获得的理解可能会导致新的轧机轧辊磨削方法来纠正几何缺陷，新的二维（而不是仅中心线）厚度控制，以及提高轧制金属几何质量的概率方法。

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.