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

职业：用于提高冷轧质量的高效动态预测模型

• 批准号: 1555531
• 负责人: Arif Malik
• 金额: $ 50万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-21 至 2021-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1555531&HistoricalAwards=false
• 关键词: CAREER; Highly; Efficient; Dynamic; Prediction

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.

这项教师早期职业发展（职业）计划奖支持基础研究，以显着提高铝，钢，铜和铜板的质量。这些金属板是许多产品的重要原材料，包括飞机，汽车，船只，冰箱，计算机，电动机和建筑物。滚动的金属板的几何质量不足需要进行昂贵的额外处理。这项研究的结果将导致新的磨坊控制策略，以提高重要新兴市场中这些金属板的滚动质量和生产力。该研究将使美国的经济竞争力和安全有益。来自代表性不足的群体（包括妇女和自闭症患者）的工程学生的直接参与将为教育和社会提供积极的好处。薄铝，钢，铜和黄铜的冷卷几何形状缺陷来自与磨机卷入式卷入式卷入式磨砂面条厚度的不匹配。详细的掷叮咬行为源于磨坊结构挠度，热膨胀，滚动研磨和滚动磨损的复杂瞬态影响，并且难以预测。这项工作中要创建的高效动态模型将使用一种新型的混合有限元方法来整合结构，热和磨损模式，以确定滚动厂的三维高频非线性非线性动态响应。这些模型将允许使用单一自由度或全尺度有限元模型进行更有效，准确的磨机动力学预测。有效的非线性，多元自由度动态模型也将与贝叶斯/马尔可夫链蒙特卡洛控制方法结合使用，尽管有显着的随机过程变化，但仍能更好地了解磨坊行为。获得的理解可能会导致新的磨砂膏磨削方法纠正几何缺陷，新颖的二维（而不是仅中心线）仪表控制以及概率方法，以提高滚动金属的几何质量。

项目成果

Efficient Three-Dimensional Model to Predict Time History of Structural Dynamics in Cold Rolling Mills

用于预测冷轧机结构动力学时程的高效三维模型

• DOI: 10.1115/1.4052703
• 发表时间: 2022
• 期刊: Journal of Manufacturing Science and Engineering
• 作者: Patel, Akash;Malik, Arif;Mathews, Ritin
• 通讯作者: Mathews, Ritin

Influence of work-roll grinding error and high-fidelity corrective grinding in cold sheet rolling

• DOI: 10.1007/s00170-022-09228-7
• 发表时间: 2022-02
• 期刊: The International Journal of Advanced Manufacturing Technology
• 作者: Akash M. Patel;A. Malik;Fengke Zhang;Ritin Mathews