Simulation

Numerical simulation for the optimization of conventional manufacturing processes is common practice in industry, but isn’t yet fully developed for generative manufacturing processes. The simulation of powder bed fusion (PBF) especially, with their more than 130 influencing factors, poses major challenges. Nevertheless, the methods developed can substantially accelerate product development, as an examination of common procedures and innovative approaches shows.

Production simulations involve considerable effort, among other things, due to the knowledge transfer between the domain expert and the simulation specialist. For small and medium-sized companies, this often represents an economic hurdle in the use of simulation. In this article, a method for a software- supported cooperation between the production expert and the simulation specialist is presented, which leads to a considerable reduction in effort. This means that the advantages of simulation can be used economically even with low optimization potentials.

This paper deals with the concept of the Industrial Metaverse and its potential impact on the manufacturing industry. First, the possibilities of the Industrial Metaverse are explained in general and then possible resulting functionalities for production technology along the life cycle are presented. For the two topics "Synthetic Data Generation" and "Virtual Qualification" the implications of the Industrial Metaverse are considered more concretely.

Bedingt durch globale Entwicklungen hinsichtlich der Preise und Versorgungssicherheit im Energiesektor stehen besonders energieintensive Unternehmen vor großen Herausforderungen. Zusätzlich fordern Kunden mehr Informationen über Energiekennzahlen und CO₂-Emissionen sowie ressourcenschonendere Prozesse. Mit einer energiedatenbasierten Simulationsmethode werden die Ressourceninformationen direkt aus dem Energiedatenmanagementsystem (EDMS) extrahiert und weiterverarbeitet. Hierbei werden sowohl aktuelle als auch stetig aktualisierte historische Daten verwendet, die automatisiert abgeglichen werden. Die digitale Abbildung der vorhandenen Prozesse ist lediglich auf Seiten der Energiedaten notwendig, ohne die technischen Prozesse in ihrer Gänze analysieren zu müssen. Mit dem so erstellten energetischen digitalen Schatten lassen sich Energiebedarfe für bevorstehende Produktionen und Produkte simulieren und können durch automatisierte Vorschläge in der Produktionsplanung positiv ...

In selective laser melting, metal powder is melted layer by layer and fused with the already manufactured part. Within this process, defective layers are created, which can be avoided. Such defects can only be detected by various compression and tensile strength experiments after printing is complete. This procedure is costly and inefficient. Therefore, the authors would like to present a demonstrator which, with the help of machine learning methods which draw from sensor-based data acquisition, is able to detect faulty layers during the manufacturing process itself and to support the machine supervisor with decision recommendations.

Precise stochastic demand calculation is the key to successful material planning, i. e. to always have exactly the right quantity on hand. However, decision-makers are faced with the dilemma of which of the many forecasting methods they should use, adapted to the item properties as much as possible. This paper examines the optimization potential of a self-developed automatically optimizing forecasting approach based on ten common forecasting methods, which are evaluated using two case studies from the capital goods industry.