CNN has been widely used to develop DL models managing imagery datasets in several application fields, being a suitable tool to work with pre-processed 3D voxelized datasets. In the present work, and for the first time to our knowledge in the field of battery manufacturing, we use a DL approach based on a 1D-CNN to learn on time-series data. The proposed 1D-CNN architecture to train the time-series is depicted in Figure . For our approach, we take advantage of CNN image-processing method but applying the network to a very different set of 1D numeric arrays, even though our data dimension is originally a 3D voxelized data. Therefore, the original 3D voxelized data is re-shaped into a 1D numeric array for every time step (input layer). Through this reshape, in terms of local environment, voxels are still representing correlated local features, adding that we can now connect them with consecutive time-series.