There are four main approaches for transferring knowledge: feature-based, instance-based, parameter/model-based and relational/adversarial-based. By adjusting the weights of individual instances (data points), instance-based transfer bridges the gap between the source and target domains, even when the overall data distributions differ. Feature-based transfer focuses on transferring the feature representation learned from the source domain to the target domain, while parameter-based transfer directly transfers the weights and biases learned by the pre-trained model as a starting point for the target domain model. In NN models, this approach freezes some layers and/or finetunes layers and/or adds some fresh layers to the original model. Finally, relational-based TL approaches transfer logical relationships or rules learned from the source domain to the target domain and are usually connected to generative adversarial networks (GANs). In this present work, we propose a simple TL approach to understand how relationships between manufacturing parameters and electrodes properties can be transferred to different chemistries with experimental data and to different volume sizes with stochastically generated data (Figure ). This proposed TL approach allows dealing with small datasets, which is commonly the case in academic research laboratories. In the experimental demonstration, we use a larger Graphite dataset to train a NN and transfer it to Si-Gra and NMC smaller datasets. In the case GDLs, we use a small volume dataset with lots of calculation to train a NN and transfer it to a larger volume dataset with less calculations that are computationally more expensive. It can be highlighted that none of these datasets were specifically generated with the purpose of training a ML model. Therefore, its distributions are not homogeneous, but our approaches herein allow us to obtain good results even with this aspect, which shows the robustness of the method. In the following Section, we explain our findings in applying these approaches to our manufacturing process experimental dataset on LIBs cells and stochastically generated dataset on PEMFCs.