Detecting Steel Surface Properties using Machine Learning

使用机器学习检测钢材表面特性

• 批准号: 2439581
• 金额: --
• 依托单位: Swansea University
• 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: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2439581
• 关键词: Detecting; Steel; Surface; Properties; using

In the production of steel coils, the temper rolling (finishing) process imprints a variety of surface and texture properties into the resulting steel. The temper rolling and resulting surface properties have a large impact on what the steel can be used for. It impacts the steel's press performance later when it is shaped and impacts how it looks when painted. Therefore, it is important that the temper rolling process is set correctly and measured to ensure desirable properties. However, surface measurements are taken at the final stage of production, using tried and tested, but slow devices. These devices are not capable of measuring the steel's surface at the speed at which it is produced. This means that it takes a long time to discover whether the steel that is being produced has the correct texture or if the rollers needs adjusting.In this project we aim to define a method of machine learning to detect and correct erroneous surface measurements to allow for inline measurement sensors to be used with confidence and speed during the rolling process. We also aim to define a methodology for classifying texture properties based on the 2D line profiles and 3D depth data from the steel surface.

在钢卷的生产中，调质轧制（精加工）工艺在所得钢材中留下了各种表面和纹理特性。调质轧制和由此产生的表面性能对钢材的用途有很大影响。它会影响钢材成型后的压制性能，并影响其涂漆后的外观。因此，正确设置和测量调质轧制工艺以确保所需的性能非常重要。然而，表面测量是在生产的最后阶段使用经过尝试和测试但速度较慢的设备进行的。这些设备无法以钢的生产速度测量钢的表面。这意味着需要很长时间才能发现所生产的钢材是否具有正确的纹理或者辊子是否需要调整。在这个项目中，我们的目标是定义一种机器学习方法来检测和纠正错误的表面测量，以便在轧制过程中能够自信和快速地使用在线测量传感器。我们还旨在定义一种基于钢表面的 2D 线轮廓和 3D 深度数据对纹理属性进行分类的方法。