Engineer Factory Automation
The Overhead Hoist Transfer System (OHT-S) as an integral part of the AMHS executes frequent transportation tasks in semiconductor factories. The OHT-S comprises of vehicles (OHV) and a rail network. To prevent congestions and delays in production, reliable and secure functionality of all single rail sections and OHV is essential. Hence, components are maintained preventively. However, this timebased procedure is resource-intensive in both material and working time aspects.
In a joint research project between TU Dresden and GLOBALFOUNDRIES, we focused on automatic condition monitoring of the OHT rail network and predictive maintenance of the OHV. In order to identify deficiencies within the rail system we captured the rail’s geometry with optical sensors and implemented an algorithm based on artificial neural network (ANN). Furthermore, we established a condition monitoring system for the OHV, which can identify and evaluate attributes of the OHVs present condition. Using a MATLAB® based algorithm, we can predict the residual lifetime of OHV components and thus plan maintenance more efficiently.
In conclusion, our condition monitoring system including analyzing algorithms provide information of the current state of the OHT system and enable a needsbased, resource-saving and efficient predictive maintenance procedure.
Since 2019 - GLOBALFOUNDRIES: Integrated Manufacturing & IT
- Administration of AMHS Applications & HW - SW Integration
Until 2019 - Technical University of Dresden, Chair of Material Handling
- Degree in Mechatronics, Dipl.-Ing.
Since 2018 - R&D for EU funded Responsive Fab project
- GLOBALFOUNDRIES & Technical University of Dresden
2017/2018 - Trainee with Mercedes-Benz U.S. International, Alabama
- Quality Engineering on Advanced Driver Assistance Systems (ADAS), 7 months
2017 - Student assistant at Chair of Dynamics and Mechanism Design, TUD
- R&D with KUKA youBot, 6 months