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WiR Augsburg


Wissenstransfer Region Augsburg

Start date: 01.01.2018
Funded by: BMBF (Bundesministerium für Bildung und Forschung)
Local head of project: Prof. Dr. Wolfgang Reif Dr. Alwin Hoffmann
Local scientists: Christian Eymüller
Julian Hanke
External scientists / cooperations: MRM - Institute of Materials Resource Management
Fraunhofer IGCV - Fraunhofer-Einrichtung für Gießerei-, Compsite- und Verarbeitungstechnik
DLR - Deutsches Zentrum für Luft- und Raumfahrt

Abstract

The project “WiR Augsburg” aims to strengthen the knowledge transfer between the University of Augsburg and medium-sized regional enterprises to face the major challenges rising due to the progress of digitization in the field of production. To overcome these challenges an innovation laboratory with the subject of “Materials Meet Automation (MMA)” will be established. Within this laboratory the area of process control of material-oriented production processes e.g. for fiber reinforced polymers is considered in particular. The activities focus on self-learning machines, sensor technology and “smart materials”.

Description

WiR Augsburg is part of the BMBF program "Innovative Hochschule". Together with our partners MRM, Fraunhofer IGCV and DLR our research focus lies on the following two topics.

Robotic component inspection

Especially in the production of fiber-reinforced plastics a considerable amount of component-level testing takes place to ensure optimal build quality. Due to the uniqueness of the manufactured components this often entails high costs for the installation of conventional test benches. Additionally, such test benches are rarely flexible enough to map the actual load conditions of the component during operation. The interdisciplinary research team comprised of members of both the ISSE and MRM research groups aims to develop systems for automatic component inspections. This includes a test bench based on an axial load unit in combination with two heavy-duty robots (see Fig. 1) which offers entirely new possibilities for the implementation of component tests with extremely flexible external load vectors, which can convincingly demonstrate the performance of secondary test methods, such as image based load determination. To generate a base load (tensile or compressive) the test platform is equipped with an axial load unit. Two 6-axis heavy-duty robots are used in conjunction with the base load of the axial unit to achieve the desired force on the workpiece. All three actuators are equipped with force-torque sensors, which allows for measurement of the respective load vectors. For precise plant management a common control concept will be implemented. The secondary test methods (e.g. image correlation, acoustic emission analysis) are further essential components of the system and provide data on the local strain states, as well as the resulting damage and thus allow comparison between calculated and real stresses.

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Fig 1: Experimental setup of the robotic component inspection

Plug-&-Work for material processing

With the help of this pilot project companies will be able to use their equipment, machines and plants in concert with Industry 4.0 technologies to enable Plug-&-Work approaches in the field of composite materials. This includes e.g., methods for developing suitable data and electrical interfaces in order to integrate the individual devices of a processing cell into the overall system. To ensure the quality of the product, processed data is used to readjust the machining process. For this purpose data and control interfaces between robots, its controllers and sensors are necessary. In addition appropriate software and data modeling for innovative materials must be developed in order to encapsulate all information about the manufacturing process. An additional aspect is the integration of data analytics into every structural component of a plant. On the one hand it shall be examined how databases can be generated and analyzed depending on their shape. In particular when processing novel materials, it is often difficult to generate large amounts of data since there are only few empirical values due to the small quantities produced. On the other hand it shall examine how the results can be automatically and platform-agnostically returned to a machining process.

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Fig 2: Plug-&-Work experimental setup

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