In the first application, the experimental dataset consisted of manufacturing process data of LIB cells with different AM chemistries. The approach consisted of using a vast Graphite dataset to train NNs to predict electrode density and mass loading. These NNs were trained using as feature the following manufacturing parameters: weight percentage of AM and solid content in the formulation, coating speed and comma gap during coating, and roll speed and roll gap during calendering. The architecture of the NNs was designed differently for each target property. These NNs performed well in the Graphite dataset, but their performance metrics diminished when used to extrapolate in the smaller datasets of Si-Gra and NMC.