The aim of the project is to research and adapt methods from the field of visual analytics and machine learning to automate quality control and quality assurance of glass items. The overarching goal is to gain insight into how to significantly reduce setup- and tuning times for automated control systems for a large variety of items and to better and more quickly understand and classify defects and manufacturing problems.
