HANNOVER MESSE 2018,
23 - 27 April
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..
Attendance free of charge to trade fair ticket holders
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23 Apr. 2018 - 26 Apr. 2018
As urbanization increases, topics such as sustainable and efficient use of energy as well as local emissions are becoming increasingly ...
23 Apr. 2018 - 27 Apr. 2018
Industrie 4.0 requires answers and solutions for different topics: Man and labour, business - and strategy examples, how to handle the ...
23 Apr. 2018, 10:00 AM - 10:10 AM
The CAE Forum shows what can be accomplished in numerical simulation, additive manufacturing (3D printing) and 3D Visualisation. ...