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

提出的研究项目涉及的核心研究问题是机器学习（ML）方法的可用性通过合成生成的训练数据集在径向环轧制（RARR）领域。对于机器学习在生产领域的使用，如RARR，必须记录好件和不合格品的数据集。在这里，平衡的数据集是有利的，关于良好和不良部分的比例。然而，对于工业获取的数据集来说，情况并非如此。因此，在本研究项目中采用了通过模拟生成的合成数据来增强数据的方法。然而，对于RARR过程，不存在快速的分析模拟，通过这种模拟可以生成足够多的具有“几何或工艺中的滚动缺陷”的综合生成数据集。因此，研究的问题是，类似的过程在多大程度上可以用于向RARR的转移。在这里，冷轧环是一种合适的工艺。其优点是由于在RARR中完全径向成形而不是在径向和轴向平行成形而降低了复杂性，并且减少了冷轧环时的温度影响。冷轧对研究项目的一个中心优势是由成形和加工工艺主席（FF）开发的冷轧过程的半解析模型。采用半解析法对冷轧过程进行快速、精确的模拟，以获得工艺数据。这种方法将在第一个项目阶段使用，以确定是否可以将合成生成的数据集用于机器学习的训练。这些结果被推广到RARR，并以环轧制ML的实验环境的形式定义。学习算法需要集成领域知识来支持，加速过程预测或根据过程特定的限制施加限制。另一个核心问题是真实数据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工艺。通过成形和加工工艺主席（FF）和生产系统主席（LPS）的合作，可以解决经典模拟方法和ML交叉的新研究问题以及研究转移的新可能性。将冷环轧制半解析模型及其向RARR （FF）的扩展与RARR （LPS）领域中基于数据驱动方法的形状缺陷研究与评价相结合，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.