Melanie Zimmer B.Sc., M.Sc.
PhD Research Student
Melanie studied at the University of Applied Sciences in Stuttgart, where she achieved her B.Sc. in Mathematics in 2013 and her M.Sc. in Software Technology in 2015. She wrote her Masters thesis with the High Speed Sustainable Manufacturing Institute (HSSMI) on data visualisation towards an augmented manufacturing reality.
She joined HSSMI as an Engineer after finishing her Masters degree, where she worked on various research and commercial projects until July 2017.
Melanie joined the Centre as a PhD student in October 2015. Her research is sponsored by the Doctoral Training Centre in Embedded Intelligence (CDT-EI) and HSSMI. Melanie's research interests lie in sustainable manufacturing, machine learning and decision support.
PhD Thesis Title: Decision-Support Framework for System Ramp-up Towards Improving Production Sustainability
With the vision of the fourth industrial revolution (Industry 4.0), more production data becomes available. This creates opportunities for manufacturers to make more informed decisions for improving the sustainability of manufacturing processes.
This is in particular important for production ramp-up, where production processes are neither well understood nor easily repeatable for other ramp-up scenarios and adjustments to the system heavily rely on human knowledge. Therefore, it is not surprising that learning from human experience and machine data have been identified in literature as an important aspect to address the complexity of ramp-up. In this context, the vision of this research work is to help to reduce the ramp-up effort and ultimately shorten ramp-up time for plug-and-produce (P&P) assembly systems.
This will be achieved through enabling a structured data and information capture during the ramp-up process, and by classifying knowledge and actions taken by the human operator at that stage. The main objective will be to create a decision-support framework, which will help a human operator in making adjustments to the equipment and processes of the ramped up system.