HANNOVER MESSE 2019,
Major advances have been made recently in the field of machine learning (ML) and specifically deep learning following decades of intensive research. The consequence of this sudden progress extends to almost every industry and the field of material science is no exception.
In this presentation we use computer vision techniques for the quality assessment of large prismatic Li-ion batteries. The performance of a Li-ion battery is intrinsically linked to the electrode microstructure. Quantitative and qualitative measurements of key structural parameters will enable optimization as well as motivate systematic numerical studies for the improvement of the battery performance. The aim is to automatically evaluate various cell components (cathode, anode, and separator) geometrically in high-resolution and to also identify various types of defects in the microstructure such as metal particle contamination, layer deformation, cracks etc..
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