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Electrode manufacturing is one of the most critical processes within the energy storage production chain, as the electrodes coating and drying quality determines the later performance of the energy storage system in large part. Critical quality losses within the electrode coating are often optical visible, but cannot be automatically and inline inspected. Often small defects like agglomerates, capillary cracks or pinholes in the carbon or metal oxide based layers have to be detected at high web speeds. In the work described in this paper it was examined which kind of features and defects can be recognized by two optical methods: visual inspection and 3D measuring. A visual camera in conjunction with a white illumination and a 3D data laser line system has been used. Image processing algorithms are applied on the scanned data to detect pinholes and agglomerates. Also thickness of coating, gradient of the coating edge and form anomalies can be determined out of the produced data. Reliable evaluation of pinholes even of small sizes has been proofed. Particle agglomerations are more challenging. To achieve good enough data for 3D evaluation standard sensors are often insufficient, due to the necessary resolution and the production speed that requires high scanning frequency. In order to provide an automated, digitalized production and inspection system, the devices were coupled to a central experiment management system. Their operation is controlled non-locally in the cloud and synchronized with the execution of experiments. The acquired raw data is stored for later evaluation and long-term archiving.
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