Simulation

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.

Additive manufacturing, i.e. the printing of three- dimensional workpieces from different materials, offers the possibility of quickly producing functional prototypes. Digital optimisation is an important building block for the rapid implementation of functional product ideas. Based on digital models, the product is virtually optimised and continuously improved. Once the product has been digitally optimised in terms of its properties, it is checked and, if necessary, adapted for additive manufacturing. The product is then manufactured, reworked and finally tested. The article shows the optimisation possibilities using the example of a dispenser from the food industry. An existing component is digitised, a flow optimisation is carried out on the digital model and the improved product is additively manufactured.

When selecting a new refrigerator, energy efficiency is a decisive selection criterion. However, in the strategic and tactical planning of value-added networks, this is not yet the case. The E²-Design-toolbox enables energy efficiency to be considered in the planning process of production and logistics networks, in addition to the classic performance and cost variables. The early integration allows to draw on the overall potential. This paper presents the underlying energy data, the optimization modules, and the user’s perspective.