GOALI/IUCRP: Modeling, Simulation, and Optimization of Strip Casting Processes

9416304萨海薄板带铸造工艺适用于不锈钢和普通碳钢，并在世界各地的公司正在开发铝合金。虽然工业成员已经证明了经验上的可行性，但该工艺还没有得到充分的理解，无法为稳健的制造操作提供工艺参数，以实现最大产量。在本研究中，将进行数值模拟，并与铝和钢的单辊和双辊连铸机的工业数据进行验证。包络模型可以预测冷却速度，冷却速度控制组织，从而决定铸钢带的最终力学性能。这项研究将与天普大学和德雷塞尔大学进行类似的研究。在目前的制造实践中，用于制造汽车车身的薄钢板和用于厨房的薄铝箔都是从大铸锭开始的，并经过多次轧制以降低厚度。这个过程包括几个制造步骤，消耗大量的能源，增加在制品（库存），并减慢交付给客户的速度。这种新颖的方法可以直接铸造带材，如果理解得好，并且在不牺牲产量的情况下实现高度可靠的控制，就有可能显著降低制造成本。由于原金属行业的风险很高，国际竞争非常激烈。在这项研究中，与国内钢铁和铝工业伙伴的拟议合作将极大地帮助研究内容和制造相关性。

9416304 Sahai Thin Strip casting process for stainless and plain carbon steel, and aluminum are under development in companies around the world. While empirical feasibility has been demonstrated by industrial members, the process is not sufficiently well understood to provide the process parameters for robust manufacturing operation to achieve maximum yield. In this research, numerical simulation will be carried out and verified with industrial data from both Single Roller as well as Twin Roller Strip Casters in aluminum as well as steel. The ensuping model can predict the rate of cooling which controls the microstructure which in turn determines the final mechanical properties of the cast strip. This research will dove tail with similar effort at Temple University and Drexel University. In the current manufacturing practice, thin steel sheets used to build car bodies and thin aluminum foils used in the kitchen, start as large ingots and are rolled numerous times to bring the thickness down. This process involves several manufacturing steps consuming lots of energy, adding to work in process (inventory) and slowing delivery to customers. This novel approach of casting strips directly if well understood and controlled to a high degree of reliability without sacrificing yields has the potential to reduce the manufacturing cost significantly. International competition is intense since the stakes are high for primary metals industry. Proposed collaboration with domestic steel and aluminum industrial partners in this research will immensely help both research content and manufacturing relevance.