This study addresses a significant gap in the field of time series regression modeling by highlighting the central role of data augmentation in improving model accuracy. The primary objective is to present a detailed methodology for systematic sampling of training datasets through data augmentation to improve the accuracy of time series regression models. Therefore, different augmentation techniques are compared to evaluate their impact on model accuracy across different datasets and model architectures. In addition, this research highlights the need for a standardized approach to creating training datasets using multiple augmentation methods. The lack of a clear framework hinders the easy integration of data augmentation into time series regression pipelines. Our systematic methodology promotes model accuracy while providing a robust foundation for practitioners to seamlessly integrate data augmentation into their modeling practices. The effectiveness of our approach is demonstrated using process data from two milling machines. Experiments show that the optimized training dataset improves the generalization ability of machine learning models in 86.67% of the evaluated scenarios. However, the prediction accuracy of models trained on a sufficient dataset remains largely unaffected. Based on these results, sophisticated sampling strategies such as Quadratic Weighting of multiple augmentation approaches may be beneficial.

Die datenbasierte Prozessüberwachung ermöglicht es, Fräsprozesse präzise zu analysieren und Anomalien frühzeitig zu erkennen – ohne zusätzliche Sensorik nachrüsten zu müssen. Durch den Einsatz spezialisierter Signalvorhersagemodelle und selbstlernender Mechanismen lassen sich variierende Produktionsbedingungen effizient abbilden, wodurch die Gesamtanlageneffektivität und Fertigungsqualität in Produktionsumgebungen mit hoher Variantenvielfalt gesteigert werden können.

The rapid individualization of milling operations have introduced unprecedented complexity and variant diversity, necessitating adaptive process monitoring under scarce-data conditions. This systematic literature review (SLR) takes a comprehensive look at machine learning (ML)-based approaches for process monitoring and anomaly detection in highly flexible milling processes, focusing on single-part and rapidly changeing production scenarios. The fourteen most relevant studies published since 2019 were identified by adhering to established SLR frameworks. The methods are evaluated in terms of their flexibility, data efficiency, model quality and cost-effectiveness. It is revealed by the SLR that transfer learning (TL), physics-informed ML and active learning (AL) are frequently used to address the issue of limited training data whilst improving the robustness of the model. However, there a shortcoming in the integration of multiple data-efficient training strategies within holistic frameworks. Additionally, focusing on internal machine signals could reduce the burden of monitoring systems on brownfield machines. Combining monitoring via internal machine signals with AL, TL, physics-informed ML and data augmentation offers promising research directions for scalable, low-cost process monitoring in flexible manufacturing environments.