Um das Potential der Daten aus modernen Fertigungsprozessen zu nutzen, sind Investitionen in Infrastruktur und Mitarbeiterausbildung notwendig. Die virtuelle 100 %-Prüfung zeigt wie die Wertschöpfung durch die intelligente Vernetzung von Maschinen, Prozessen und Menschen gesteigert wird. Datengestützte Qualitätsmodelle bilden den Zusammenhang zwischen Prozessdaten und Qualitätsmerkmalen des Produktes ab. Die minutenschnelle virtuelle 100 %-Prüfung führt zu einer frühzeitigen Identifikation von Ausschuss und Prozessschwankungen. Um das Vertrauen in die Qualitätsmodelle zu stärken, wird eine Vorhersageunsicherheit mittels probabilistischer Algorithmen quantifiziert. So kann die Fähigkeit des virtuellen Prüfprozesses analog zu Normen und Standards der Industrie ermittelt werden. Anhand eines Versuchsstands am Werkzeugmaschinenlabor WZL der RWTH Aachen wird die Wertschöpfungssteigerung durch Digitalisierung an einer zweistufigen Prozesskette demonstriert.

In the context of industrially mass-manufactured products, quality management is based on physically inspecting a small sample from a large batch and reasoning about the batch's quality conformance. When complementing physical inspections with predictions from machine learning models, it is crucial that the uncertainty of the prediction is known. Otherwise, the application of established quality management concepts is not legitimate. Deterministic (machine learning) models lack quantification of their predictive uncertainty and are therefore unsuitable. Probabilistic (machine learning) models provide a predictive uncertainty along with the prediction. However, a concise relationship is missing between the measurement uncertainty of physical inspections and the predictive uncertainty of probabilistic models in their application in quality management. Here, we show how the predictive uncertainty of probabilistic (machine learning) models is related to the measurement uncertainty of physical inspections. This enables the use of probabilistic models for virtual inspections and integrates them into existing quality management concepts. Thus, we can provide a virtual measurement for any quality characteristic based on the process data and achieve a 100 percent inspection rate. In the field of Predictive Quality, the virtual measurement is of great interest. Based on our results, physical inspections with a low sampling rate can be accompanied by virtual measurements that allow an inspection rate of 100 percent. We add substantial value, especially to complex process chains, as faulty products/parts are identified promptly and upcoming process steps can be aborted.

Federated learning enables multiple organizations to collaboratively analyze data while retaining local control over their datasets, making it attractive for applications such as healthcare. Distributed quantum computing provides a natural framework for such workflows, allowing geographically separated quantum processors to execute joint computations using shared entanglement while preserving data privacy. In this work, we investigate the feasibility of satellite-enabled distributed quantum computing for federated quantum learning. As a representative application, we consider a distributed distance-based quantum classifier in which multiple parties contribute local data through quantum operations. To support this application, we develop a hybrid space-ground quantum network architecture in which satellites distribute entanglement between distant ground stations. The communication layer is combined with a noise-aware neutral-atom processor model, enabling a system-level analysis that captures both network and hardware constraints. Simulation results across varying network sizes, feature dimensions, and coherence regimes show that classifier performance is jointly determined by communication resources, processor noise, and data representation. In low-coherence regimes, decoherence destroys the classifier’s discriminative signal, whereas high-coherence regimes reveal limitations arising from feature-space conditioning and feature redundancy. These results demonstrate that satellite quantum networks could support distributed quantum learning over long distances, while highlighting the importance of coherence time, entanglement-distribution performance, and learning-aware data encoding for future large-scale deployments.