Kugelgewindetriebe (KGT) weisen abhängig von ihrer Geometrie und der vorherrschenden Belastung deutlich variable innere Lastverteilungen auf. Finite-Elemente-Modelle erlauben die Ermittlung dieser Verteilungen, die aktuell jedoch vor allem bei der Lebensdauerberechnung nur unzureichend berücksichtigt werden. Nachfolgend wird eine Methode zur Berücksichtigung ortsdiskreter Lastverteilungen in der Lebensdauerberechnung von Kugelgewindetrieben vorgestellt.   Ball screws (BS) exhibit strongly varying internal load distributions depending on their geometry and the applied load. With finite element models this distribution may be estimated, however, distributed loads are barely taken into account in applications such as service life calculation. In the following, a method to integrate discrete location load distributions into the service life calculation of ball screws is presented.

Der Spindelsteigungsfehler weist einen signifikanten Einfluss auf das Betriebsverhalten von Kugelgewindetrieben auf. In diesem Beitrag wird ein Simulationsmodell vorgestellt, mit dem der Einfluss des Steigungsfehlers auf die interne Lastverteilung, die Steifigkeit und die Lebensdauer quantifiziert werden kann. Mithilfe des Modells werden im Anschluss an die aktuelle Auslegungspraxis nach Norm angelehnte Korrekturfaktoren zur Berücksichtigung des Spindelsteigungsfehlers berechnet.

The main objectives in production technology are quality assurance, cost reduction, and guaranteed process safety and stability. Digital shadows enable a more comprehensive understanding and monitoring of processes on shop floor level. Thus, process information becomes available between decision levels, and the aforementioned criteria regarding quality, cost, or safety can be included in control decisions for production processes. The contextual data for digital shadows typically arises from heterogeneous sources. At shop floor level, the proximity to the process requires usage of available data as well as domain knowledge. Data sources need to be selected, synchronized, and processed. Especially high-frequency data requires algorithms for intelligent distribution and efficient filtering of the main information using real-time devices and in-network computing. Real-time data is enriched by simulations, metadata from product planning, and information across the whole process chain. Well-established analytical and empirical models serve as the base for new hybrid, gray box approaches. These models are then applied to optimize production process control by maximizing the productivity under given quality and safety constraints. To store and reuse the developed models, ontologies are developed and a data lake infrastructure is utilized and constantly enlarged laying the basis for a World Wide Lab (WWL). Finally, closing the control loop requires efficient quality assessment, immediately after the process and directly on the machine. This chapter addresses works in a connected job shop to acquire data, identify and optimize models, and automate systems and their deployment in the Internet of Production (IoP).