Although digital twins are increasingly used for pre-deployment testing, their reliability as predictive tools remains understudied due to the lack of established validation frameworks. This paper presents a systematic methodology for validating the predictive fidelity of physics-based digital twins in robotic navigation tasks, addressing a critical gap in sim-to-real transferability for industrial mobile manipulators. We propose a novel evaluation approach combining (1) multi-metric comparison (localization accuracy, path consistency, goal accuracy, and navigation performance) between real-world and simulated navigation experiments, and (2) an uncertainty quantification method to establish confidence intervals for digital twin predictions. Using an NVIDIA Isaac Sim model of an omnidirectional mobile manipulator and digitally reconstructed production environments, we conduct 50 real-world and 50 digital twin experiments across five industrial scenarios. The results show a mean Hausdorff distance of 0.195 m between real and simulated paths, localization RMSE differences of 0.005 m, and a path prediction accuracy of ±0.229 m (95% CI). The findings contribute to robotic navigation by addressing key challenges in using digital twins for real-world applications, reducing the sim-to-real gap, and enhancing the reliable deployment of mobile manipulators in flexible assembly systems.

Purpose: The determination of the measurement uncertainty is relevant for all measurement processes. In production engineering, the measurement uncertainty needs to be known to avoid erroneous decisions. However, its determination is associated to high effort due to the expertise and expenditure that is needed for modelling measurement processes. Once a measurement model is developed, it cannot necessarily be used for any other measurement process. In order to make an existing model useable for other measurement processes and thus to reduce the effort for the determination of the measurement uncertainty, a procedure for the migration of measurement models has to be developed.Design/methodology/approach: This paper presents an approach to migrate measurement models from an old process to a new “similar” process. In this approach, the authors first define “similarity” of two processes mathematically and then use it to give a first estimate of the measurement uncertainty of the similar measurement process and develop different learning strategies. A trained machine-learning model is then migrated to a similar measurement process without having to perform an equal size of experiments.Similarity assessment and model migrationFindings: The authors’ findings show that the proposed similarity assessment and model migration strategy can be used for reducing the effort for measurement uncertainty determination. They show that their method can be applied to a real pair of similar measurement processes, i.e. two computed tomography scans. It can be shown that, when applying the proposed method, a valid estimation of uncertainty and valid model even when using less data, i.e. less effort, can be built.Originality/value: The proposed strategy can be applied to any two measurement processes showing a particular “similarity” and thus reduces the effort in estimating measurement uncertainties and finding valid measurement models.

Currently, aeroplane assembly is carried out in rigid, aeroplane-specific assembly lines which causes high costs for reconfiguration. One approach to reduce the costs is to use automated lineless assembly systems. To enable automated reconfiguration of assembly processes, continuous availability of factory-wide position data is required. When providing these data, a challenge is the combination of large-volume, “global” measurement sensors and high-precision, “local” measurement sensors to a single easily interpretable indication. The aim of this research work is the fusion of local and global measurement data within a so-called Global Reference System (GRS). For data fusion, sensors are linked to a reference measurement system covering the entire assembly area. The relationships between the coordinate systems of the individual measurement systems are described using homogeneous transformations. Using Kalman filters, the transformed measurement data are fused into a vector containing information about the pose of aeroplane components. Furthermore, a GUM-compliant statement about the measurement uncertainty of the fused sensor data is made by specifying the covariance matrix. The method is validated using a demonstrator covering the essential aspects of the assembly process. The result is a validated procedure for data fusion and for the determination of the combined measurement uncertainty in a GRS.

Purpose: To ensure product quality within a manufacturing process, inspection processes are indispensable. One task of inspection planning is the selection of inspection characteristics. For optimization of costs and benefits, key characteristics can be defined by which the product quality can be checked with sufficient accuracy. The manual selection of key characteristics requires substantial planning effort and becomes uneconomic if many product variants prevail. This paper, therefore, aims to show a method for the efficient determination of key characteristics.Design/methodology/approach: The authors present a novel Algorithm for the Selection of Key Characteristics (ASKC) based on an auto-encoder and a risk analysis. Given historical measurement data and tolerances, the algorithm clusters characteristics with redundant information and selects key characteristics based on a risk assessment. The authors compare ASKC with the algorithm Principal Feature Analysis (PFA) using artificial and historical measurement data.Findings: The authors find that ASKC delivers superior results than PFA. Findings show that the algorithms enable the cost-efficient selection of key characteristics while maintaining the informative value of the inspection concerning the quality.Originality/value: This paper fills an identified gap for simplified inspection planning with the method for the efficient selection of key features via ASKC.

Zusammenfassung: Die Hauptgefahr bei minimalinvasiven Bohrungen für die Gesundheit des Patienten ist eine thermische Verletzung von Nerven- oder Knochengewebe durch einen erhöhten Wärmeeintrag. Für die prozessparallele Ermittlung der Bohrgrundtemperatur wurde ein Bohrer mit integrierter Temperatursensorik entwickelt. Diese gemessene Temperatur steht jedoch in einem unbekannten Zusammenhang mit der realen Bohrgrundtemperatur, da Unsicherheiten die Messung beeinflussen. Um die Temperatur als verlässliche Entscheidungsgrundlage während der minimalinvasiven Bohrung verwenden zu können, müssen systematische Abweichungen der gemessenen Temperatur von der realen Temperatur bekannt sein. Zufällige Abweichungen und solche systematischen Abweichungen, die nicht korrigiert werden können, müssen in einer Messunsicherheitsbetrachtung zusammengefasst werden. Zur Bestimmung der Kalibrierkurve wird ein Messaufbau entworfen, um systematische Fehler der Temperaturmessung mit dem Bohrer kompensieren zu können. Die Ergebnisse der Unsicherheitsbetrachtung zeigen, dass die Unsicherheit mit steigender Temperatur steigt. Die Unsicherheit wird konservativ mit u _T = 1 K abgeschätzt. Zur Beurteilung einer thermischen Schädigung des Gewebes wird der CEM43 herangezogen, dessen Güte jedoch in erheblichem Maß von der zugrundeliegenden Datenqualität abhängt. In einer Analyse des Einflusses der Messunsicherheit auf den CEM43 mit der Unsicherheitsfortpflanzung und Monte-Carlo-Methoden wird festgestellt, dass bereits geringe Unsicherheiten in der Temperaturmessung zu erheblichen Abweichungen des CEM43 führen. Der intraoperative Einsatz des CEM43 als Kennwert für eine thermische Gewebeschädigung ist deshalb nicht möglich.