Joshua Holstein

Background

To date, quality control in the manufacturing industry is often based on simple visual systems. However, as these systems rely on hand-crafted rules created by looking at only a few images, variations that can exhibit during production time can not be sufficiently considered. Consequently, significant amounts of good products are rejected, and products with defects remain undetected. Therefore, it is essential to assist domain experts in determining improved rules for existing visual quality control systems. To do so, vast amounts of images of collected images can be leveraged and analyzed.

Research Goal

In cooperation with Bayer AG, you will apply the design science research [1] approach to develop design knowledge for systems that can assist domain experts in analyzing large amounts of images efficiently. Therefore, you will interview domain experts to determine their requirements for such systems and implement a prototype of the system in Python.

Working on this thesis you will:

- Apply your theoretical knowledge to a practical use case in the context of manufacturing.
- Work with large amounts of images in various quality control settings.
- work directly with domain experts to improve the environmental impact of manufacturing lines.

We look forward to receiving your application because you:

- You are interested in the field of machine learning
- You are highly motivated to work on recent real-world problems in a self-organized and goal-oriented working mode and you bring in own ideas
- You are open-minded and don’t hesitate to get in touch with professionals, e.g., throughout an interview study
- Very good English skills as the thesis will be written in English

Details

 - Start: February/March

 - Duration: 6 months

 - Location: Bayer AG, Remote or nearby Grenzach-Whylen

We offer you a challenging research topic, close supervision, and the opportunity to develop practical and theoretical skills. If you are interested, send your CV, transcript of records, and a brief letter of motivation to Joshua.Holstein@kit.edu.

References:

[1] Gregor, S., & Hevner, A. R. (2013). Positioning and presenting design science research for maximum impact. MIS quarterly, 337-355.