Inline inspection of ceramic tape casting processes by means of optical, eddy current and machine learning methods

Tape casting is a ceramic forming technology used to produce planar ceramic components by means of a doctor-blade process. However, industrial casting plants are currently not equipped with inline measurement tools which allow detecting of defects and determine quality parameters of the ceramic tape. Here we use laser triangulation, camera-based monitoring, and eddy current measurement tools to acquire different tape quality parameters. Results of the implementation of these tools and the insights gained by applying machine learning algorithms for automated defect detection and classification are presented.