Simulation of Texture Development during Steel Processing

目前，开发新的钢合金需要数百吨的材料，这使得试验既缓慢又昂贵。我们已经开发了一种高通量方法（快速合金加工，RAP）来开发新钢（跨越学术界和工业界），目标是合金发现的数量级增加。为了达到许多带钢应用所需的可成形性（例如罐头和汽车车身板），需要优化钢板中的晶体结构，这需要了解冷变形加工参数与钢冷变形的显微组织状态（包括应变状态和织构）之间的关系。本博士将专注于理解当前的纹理发展模型，并在这些关系的基础上开发高成形性传统钢带等级的预测模型。

Currently developing new steel alloys requires 100s of tonnes of materials making trials slow and expensive. We have developed a high throughput approach (rapid alloy processing, RAP) to the development of new steels (spanning academia and industry) targeting an order of magnitude increase in alloy discovery. To achieve the desired formability that many applications of strip steel require (e.g. canning and automotive body panels) the crystallographic texture in the steel sheet needs to be optimised, which requires understanding of the relationships between cold deformation processing parameters and microstructural state (including strain state and texture) for cold deformation of steels. This PhD will focus on understanding the current models for texture development and build on these relationships to develop predictive models for high formability conventional steel strip grades.