PhD Thesis in Advanced Manufacturing Towards A Closed Loop Control in Additive Manufacturing

Applications are invited for a PhD position to join ETH Zurich. This position is open until filled.

Our research group in the Department of Mechanical and Process Engineering is looking for up to 2 new PhD students starting as soon as possible in the area of additive manufacturing.

Manufacturing has been and will be further disrupted by the recent advances in simulation, data science and digital twins. These technologies have a great potential to fully digitalize and improve manufacturing, rendering it more stable and cost efficient. Our research is driven by the goal to digitalize manufacturing and leverage the potential of simulations and the acquired data. This includes but is not limited to additive manufacturing technologies, as it is a good example of a fully digitalizable workflow that provides a great amount of data throughout the process. While it opens up new routes of manufacturing, it still poses a lot of challenges to be solved by simulations and an improved control of the process. To advance manufacturing, we draw upon data science and machine learning technologies to apply them to industrially relevant questions.

Toolpath generation in additive manufacturing is a key ingredient for the manufacturing process. It is until now almost completely defined upfront in a more or less simple fashion, where the area to be solidified is organized in stripes or squares and exposure vectors are distributed within. However, there are still obstacles that lower the quality of the printed part in regions of low underlying mass or in border regions where very short vectors arise. To overcome these difficulties in the printing process, one can either assign a more sophisticated print planning upfront or act upon it while printing, depending on the category of problem. Thermal monitoring then allows to observe these regions and balances their effects during the manufacturing process through countermeasures. The topic of these theses are the digital set up of the system, gaining a good physical understanding of the sensor data by accompanied simulations and using data science as well as machine learning to deduce a first set of rules to counteract deviations in the print process. You will work with a small and highly motivated team of young researchers in a very pleasant atmosphere. The salary and general working conditions are internationally highly competitive and according to ETH standards. Participation in conferences as well as collaborations and exchange programs are supported.

ETH Zurich is one of the world's leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.

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