A Data-Driven Approach to Predict the Extent of Surface Oxidation During Hot-Rolling and Annealing of New Steels

预测新钢热轧和退火过程中表面氧化程度的数据驱动方法

• 批准号: 2372348
• 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: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2372348
• 关键词: Data; Driven; Approach; Predict; Extent

The aim of this project is to deliver a profound model for the prediction of both the nature and extent of surface oxides that have been formed during the heat treatments of high strength flat steel products. The predictions will be applicable to a wide variety of industrial conditions and consider effects between the partnered industries within the EPSRC Prosperity partnership project. In addition to a quantification of the result's predictive uncertainty, the theoretical model will be used to propose an optimum manufacturing route for automotive steels in view of an optimum surface quality. The project hence overlaps with the EPSRC priority areas of "Engineering" and "Manufacturing of the Future".

该项目的目的是为预测高强度扁平钢产品的热处理期间形成的表面氧化物的性质和程度提供深刻的模型。这些预测将适用于各种各样的工业条件，并考虑EPSRC繁荣合伙项目中合作行业之间的影响。除了量化结果的预测不确定性外，鉴于最佳的表面质量，理论模型还将用于为汽车钢提出最佳的制造途径。因此，该项目与“工程”和“未来制造”的EPSRC优先领域重叠。