Dear reader,

Our value-added chains are radically changing. In 2020, it is also our job to analyze trends early on and to meet the related challenges in an application-oriented manner. You will find intriguing project results for the future of production in our current annual report. We have selected the following highlights of these project results for you.

Fuel cells stacks: progress in automated assembly

© Fraunhofer IWU

Currently one of the largest challenges for future development of fuel cell technology is the high cost in comparison to established gas and diesel engines. This high cost is mainly caused by the usage of non-standard components and the usually hardly standardized production of polymer electrolyte membrane fuel cell stacks (PEMFC stacks), which are the core of the fuel cell system. 

Monitoring system for machine tools: visualizing working accuracy and reducing inspections

© Fraunhofer IWU

A machine tool operator cannot trace which path a tool follows inside a machine during the processing of parts due to diverse prevailing influences, which are not all measurable. However, so far there have been no options to detect any path errors during processing. Therefore, regular inspections of the workpieces are necessary.

Development of a visual assistance system to optimize joining processes

© Fraunhofer IWU

The digitization of production is one of the most important fields of action to ensure future growth and employment in Germany. In this context, mechanical joining processes play an essential role. As a cross-sectional technology, mechanical joining offers innovative and cost-efficient processes to combine identical and different materials.

Skiving: Complex processes economically designed

© Fraunhofer IWU

Innovative technological approaches are required due to the industrial demand for high-performance and cost-efficient processes to produce high-quality geared components such as the components needed in sun-and-planet gears for electric mobility.  One solution to this demand consists of skiving, a gearing process using a defined cutting edge, combining the productivity of hobbing with the geometrical flexibility of gear shaping.

Laser melting processes directly monitored using Deep Learning and Big Data

© Fraunhofer IWU

Additive manufacturing processes such as powder-bed based laser melting achieve series production if they reach a reproducibly high component quality. For this reason, numerous monitoring systems have already been installed in process chains of 3D printing. To assess the uniformity of the local melt pools during component formation, photodiodes integrated into the laser’s optical path and infrared cameras are used to detect component defects early.