Development of an advanced microstructural model for strip steel phase transformation behaviour using sensor signal feedback

带钢可用于建筑、汽车、包装和能源部门。对于热轧带钢产品，控制热轧后轧板上的冷却对获得所需的组织和性能具有重要意义。已有的连续冷却图表和模型可以预测组织的发展，然而，在工业加工过程中，冷却是复杂的，热变形结束时开始的奥氏体晶粒度是可变的。因此，需要针对给定的钢成分和轧制规程对模型进行优化。电磁(EM)传感器已经开发出来，可以直接表征转变过程中的微观组织；这些传感器可以放置在跳动表中，以提供关于转变状态的数据，以便与磨机模型进行比较。该项目将专注于塔尔博特港热轧带钢厂的改造模型，初步评估不同牌号的可预测性，然后使用传感器信号反馈优化模型。

Strip steel can be used in the construction, automotive, packaging and energy sectors. For hot rolled strip product controlling the cooling on the run-out table after hot rolling is important to achieve the desired microstructure and hence properties. Continuous cooling diagrams and models exist to predict the microstructural development, however, during industrial processing the cooling is complex and the starting austenite grain size, at the end of hot deformation, is variable. Therefore, the models need to be optimised for a given steel composition and rolling schedule. Electromagnetic (EM) sensors have been developed that can directly characterise the microstructure during transformation; these sensors can be positioned in the run-out table to provide data on the state of transformation to compare to the mill models. This project will focus on the transformation model for the Port Talbot hot strip mill, initially assessing the predictability for different grades and then optimising the model using sensor signal feedback.