Detecting Steel Surface Properties using Machine Learning

在钢卷的生产中，平整轧制（精轧）工艺将各种表面和纹理特性压印到所得钢中。平整轧制和由此产生的表面性能对钢的用途有很大的影响。它会影响钢材成型后的冲压性能，并影响其涂漆时的外观。因此，重要的是，平整轧制过程的正确设置和测量，以确保所需的性能。然而，表面测量是在生产的最后阶段进行的，使用的是久经考验但速度较慢的设备。这些设备无法测量生产速度下的钢表面。这意味着要花很长时间才能发现正在生产的钢材是否具有正确的纹理，或者轧辊是否需要调整。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，以便在轧制过程中自信和快速地使用在线测量传感器。我们还旨在定义一种方法，用于分类纹理属性的基础上的2D线轮廓和3D深度数据从钢表面。

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.