本文描述了轧机骨料从独立解决方案到完全集成的网络物理生产系统的转变。在这个过程中，已经存在的称重传感器被替换，并应用了额外的电感和磁性位移传感器。校准后，这些在金属成型主席 (Montanuniversitaet Leoben) 的智能成型实验室中完全集成到六层数字化架构中。在此框架内，设计了两个前端人机界面，其中第一个作为轧制过程中的状态监控系统。第二个用户界面将弹性机器学习算法的结果可视化，该算法是使用 Python 设计的，不仅能够预测和调整所定义金属板的最终轧制计划，还要从额外的轧机进度表中学习。该算法是在黑盒方法的基础上创建的，使用来自 1900 多个具有不同辊缝高度、板材宽度和摩擦条件的铣削步骤的数据。因此，开发的程序能够在这些参数以及不同的初始板材厚度之间进行内插和外推，作为基于数据的数字孪生，针对不同轧制工艺步骤之间的进度变化提出建议。此外，通过第二个用户界面，可以可视化这些参数对铣削过程结果的影响。由于整个层系统运行在大学内部服务器上，学生和其他感兴趣的各方能够访问可视化，因此可以使用环境来加深他们对钣金轧制过程的特征和影响以及数据科学，尤其是机器学习基础知识的了解。该算法还可作为进一步整合基于材料科学的数据以预测不同材料对轧制结果的影响的基础。为此，还分析了轧制试样的塑性应变路径对其机械性能的影响，包括各向异性和材料强度。该算法还可作为进一步整合基于材料科学的数据以预测不同材料对轧制结果的影响的基础。为此，还分析了轧制试样的塑性应变路径对其机械性能的影响，包括各向异性和材料强度。该算法还可作为进一步整合基于材料科学的数据以预测不同材料对轧制结果的影响的基础。为此，还分析了轧制试样的塑性应变路径对其机械性能的影响，包括各向异性和材料强度。
This paper describes the transformation of a rolling mill aggregate from a stand-alone solution to a fully integrated cyber physical production system. Within this process, already existing load cells were substituted and additional inductive and magnetic displacement sensors were applied. After calibration, those were fully integrated into a six-layer digitalization architecture at the Smart Forming Lab at the Chair of Metal Forming (Montanuniversitaet Leoben). Within this framework, two front end human machine interfaces were designed, where the first one serves as a condition monitoring system during the rolling process. The second user interface visualizes the result of a resilient machine learning algorithm, which was designed using Python and is not just able to predict and adapt the resulting rolling schedule of a defined metal sheet, but also to learn from additional rolling mill schedules carried out. This algorithm was created on the basis of a black box approach, using data from more than 1900 milling steps with varying roll gap height, sheet width and friction conditions. As a result, the developed program is able to interpolate and extrapolate between these parameters as well as different initial sheet thicknesses, serving as a digital twin for data-based recommendations on schedule changes between different rolling process steps. Furthermore, via the second user interface, it is possible to visualize the influence of this parameters on the result of the milling process. As the whole layer system runs on an internal server at the university, students and other interested parties are able to access the visualization and can therefore use the environment to deepen their knowledge within the characteristics and influence of the sheet metal rolling process as well as data science and especially fundamentals of machine learning. This algorithm also serves as a basis for further integration of materials science based data for the prediction of the influence of different materials on the rolling result. To do so, the rolled specimens were also analyzed regarding the influence of the plastic strain path on their mechanical properties, including anisotropy and materials’ strength.
