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

使用传感器信号反馈开发带钢相变行为的先进微观结构模型

• 批准号: 2894208
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2894208
• 关键词: Development; advanced; microstructural; model; strip

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.

地带钢可用于建筑、汽车、包装和能源领域。对于热轧带钢产品，控制热轧后冷却台的冷却对于获得理想的组织和性能是非常重要的。已有连续冷却图和模型用于预测微观组织的发展，但在工业加工过程中，冷却是复杂的，热变形结束时的起始奥氏体晶粒尺寸是可变的。因此，需要针对给定的钢成分和轧制计划对模型进行优化。已经开发出可以直接表征相变过程中微观结构的电磁（EM）传感器；这些传感器可以放置在运行表中，以提供有关转换状态的数据，以便与磨机模型进行比较。该项目将重点关注塔尔伯特港热带厂的改造模型，首先评估不同等级的可预测性，然后使用传感器信号反馈优化模型。