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，为显著提高冷轧厂生产的铝、钢、铜和黄铜薄板的质量奠定基础。这些金属板是许多产品的重要原材料，包括飞机、汽车、船舶、冰箱、计算机、电机和建筑。几何质量差的轧制金属板材需要进行昂贵的额外加工。这项研究的结果将导致新的轧机控制策略，以提高这些金属板材在重要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。来自代表不足的群体(包括妇女和自闭症患者)的工程专业学生直接参与制造将为教育和社会带来积极的好处。冷轧铝、钢、铜和黄铜薄板的几何缺陷是由于输入的带钢厚度曲线与轧辊咬合曲线不匹配造成的。详细的轧辊咬合行为源于磨机结构偏差、热膨胀、轧辊磨削和轧辊磨损等复杂的瞬时效应，很难预测。这项工作将建立高效的动力学模型，利用一种新的混合有限元方法来综合结构、热和磨损模式，以确定轧钢机的三维高频非线性动态响应。与单自由度或全尺寸有限元模型相比，这些模型将允许对磨机动力学进行更有效和更准确的多自由度预测。有效的非线性、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，以更好地理解磨机行为，尽管存在显著的随机过程变化。这些认识可能会导致新的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，以及改善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.