Die additive Reparatur von beschädigten Bauteilen und Formen ist häufig mit hohen Kosten verbunden. Durch die digitale Vernetzung wurde ein Mehrwert in der additiven und spanenden Reparatur geschaffen, der sowohl die Anlagenzeit und Ingenieurszeit reduziert und gleichzeitig auf variable Genauigkeitsanforderungen übertragbar ist. Anhand einer Turbinenschaufel wurde der Reparaturprozess getestet und kann in der Zukunft auf beliebige Bauteile übertragen werden.
The additive repair of damaged components and moulds is often associated with high costs, especially for SMEs. By digitally networking different components, an added value in additive and metal-cutting repair has been created, which reduces both machine time and engineering time and is at the same time transferable to variable accuracy requirements. The repair process was tested using a turbine blade and will be transferred to individual components in the future.
In a high-mix low volume production environment, time to market is a key factor. However, one bottleneck lies in the often times manual parametrization of machines for new or modified designs. A truly flexible manufacturing environment therefore requires a continuous data flow from the design stages to the shop floor. This paper presents a concept for the automated parameterization of machines at a large automotive plant. Therefore, this paper initially discusses the results of a stakeholder analysis. The stakeholders comprise of different departments related to the product design and manufacturing processes. The requirements resulting from the interviews conducted with the stakeholders are grouped and ordered by priority. Secondly a general architecture for the control interface is presented. It includes multiple submodules, which model the continuous data flow between the departments and the production systems. The first main step of the data flow is the transformation of the information which is presented in various styles depending on the source departments to structured and standardized data. Thereafter machine parameters are generated automatically by a submodule using the structured input data and inference rules. Finally, the architecture supports the automatic transfer of the machine readable output data to the assembly line. To test the architecture a prototype comprising of more than 100 robots in a live production environment is implemented. It allows for a continuous data flow from the design and productions planning department to the robots. This enables a flexible process control, which up to today has supported the fast roll out of more than 100 new product variants. In contrast to the conventional manual setup of the machines for operation, the prototype was able to show that the monetary and time expenditure could be reduced by 95 percent.
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