Technische Universität Dresden
The TU Dresden is one of eleven German universities that were identified as an “excellence university”. TUD has about 37,000 students and 8,000 staff members – among them over 500 professors – and, thus, is the largest university in Saxony today.
TUD will be represented in PowerBase by two chairs: the Chair of Technical Information Systems (Prof. Klaus Kabitzsch) in the field of applied computer science and the Chair of Logistics Engineering (Prof. Thorsten Schmidt) in the field of logistics and mechanical engineering. The Chair of Technical Information has great know-how in real-world integration of automation systems into enterprise networks, systems modelling, dynamic modelling, simulation, condition monitoring and data analytics. The Chair of Logistics Engineering has more than 30 manyears experience in planning, analysing, modelling, simulating and optimizing layout and control of (automated) material handling systems – conveyor based as well as vehicle based.
Role:
TUD is a research service provider. The PowerBase project will benefit from our multidisciplinary team of different engineers and their recent scientific work. As a research institution, TUD will focus on applied scientific research with a higher level of risk compared to non-academic partners.
Key Contribution:
TUD will support Infineon Dresden in investigating and implementing a conveyor based automated material handling system (AMHS) by providing benchmarks in comparison with a vehicle based transport system. Second goal is to significantly shorten the time required to answer questions about the performance impact to be expected when experimenting with different wafer start scenarios. To achieve this, we will try to find novel ways for the automated design of experiments for fab capacity simulations. Furthermore, TUD will investigate the shock and vibration impact caused by the AMHS onto different sensitive materials/substrates used within power manufacturing. The goal is to reduce scrap caused by (automated) handling processes. Finally, TUD will establish a smart system performance monitoring (SSPM) for intelligent signal analysis and evaluation with the purpose of observing the variation in behaviour of specific product transportation or handling processes and report anomalies to downstream process control
systems.
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