Complementary database generation for machine learning for quality prognosis using the example of ring rolling

拟议的研究项目涉及的机器学习（ML）方法的可用性的中心研究问题，通过综合生成的训练数据集在径向-轴向环轧制（RARR）领域。为了在RARR等生产领域使用ML，必须记录好的和不合格的零件的数据集。在此，平衡的数据集在良好和不合格部件的比率方面是有利的。然而，对于工业上获得的数据集，情况并非如此。因此，在本研究项目中使用了通过模拟生成的合成数据进行数据增强的方法。然而，对于RARR过程，不存在快速分析模拟，通过该快速分析模拟可以生成具有“几何形状或过程中的轧制缺陷”的足够大数量的合成产生的数据集。出于这个原因，研究的问题是在何种程度上可以用于转移到RARR类似的过程。在这里，环的冷轧是合适的工艺。其优点是由于在RARR中仅径向成形而不是在径向和轴向方向上平行成形而降低了复杂性，以及在冷环轧制期间降低了温度影响。冷轧研究项目的核心优势是由成形和加工工艺主席（FF）开发的冷轧过程的半分析模型。这种半解析方法可以快速、准确地模拟冷轧过程，生成过程数据。这种方法将在第一个项目阶段使用，以确定合成生成的数据集是否可以用于ML的训练。这些结果概括为RARR和定义的形式ML环轧制的实验环境。学习算法应得到领域知识集成的支持，以加速过程预测或根据过程特定限制施加限制。另一个中心问题是真实的和合成数据所需的数据质量，以及数据质量的差异如何反映在过程预测中。在第二个项目阶段，研究结果将转移到热轧过程中。通过成形和加工工艺主席（FF）和生产系统主席（LPS）的合作，可以解决经典仿真方法和ML交叉点的新研究问题以及研究转移的新可能性。冷辗扩半解析模型及其扩展到RARR（FF）与通过RARR（LPS）领域中的数据驱动方法对形状缺陷的研究和评估相结合，形成了一种新的机器学习方法。

The proposed research project deals with the central research question of the usability of machine learning (ML) methods by means of synthetically generated training data sets in the field of radial-axial ring rolling (RARR). For the use of ML in production areas such as RARR, data sets of good and reject parts have to be recorded. Here, balanced data sets are advantageous with regard to the ratio of good and reject parts. However, this is not the case for industrially acquired data sets. Therefore, the approach of employing data enhancement by synthetic data generated by simulations is used within this research project. For RARR processes, however, fast analytical simulations do not exist by which a sufficiently large number of synthetically produced data sets with "rolling defects in geometry or process" can be generated. For this reason, the research question is investigated to what extent a similar process can be used for the transfer to RARR. Here, the cold rolling of rings is a suitable process. The advantage is the reduction of complexity due to the exclusively radial forming instead of the parallel forming in the radial and axial direction in the RARR as well as the reduced temperature influence during cold ring rolling. A central advantage of cold rolling for the research project is the semi-analytical model of the cold rolling process developed by the Chair of Forming and Machining Processes (FF). This semi-analytical method is used to simulate the cold rolling process quickly and precisely in order to generate process data. This approach will be used in a first project phase to determine if synthetically produced data sets can be used for the training of ML. These results are generalised for RARR and defined in form of an experimental environment for ML in ring rolling. The learning algorithm shall be supported by the integration of domain knowledge to accelerate the process prediction or to impose limits according to process specific limits. Another central question is the required data quality of the real and synthetic data and how differences in data quality are reflected in the process prediction. In the second project phase, the findings will be transferred to the hot rolling process. Through the cooperation of the Chair of Forming and Machining Processes (FF) and the Chair of Production Systems (LPS) new research questions at the intersection of classic simulation methods and ML and new possibilities for research transfer can be addressed. The combination of the semi-analytic model of cold ring rolling and its extension to RARR (FF) with the research and evaluation of shape defects by means of data driven methods in the field of RARR (LPS) forms the approach for a new method of machine learning.