This ML model (acting as a surrogate model of the PBM) will basically be calculating the results based on ML-derived synthetic data, but if parametrized correctly, it would be able to help the process engineers to make decisions on whether to change a step of the process or leave it unchanged according to the predicted results. To go even further in the automation dream, another model could be coupled to the fully automated line, in order to be smart enough to tweak the machine specifications if it detects at some point that the measured parameters has a negative correlation to the battery performance. In this way, we could have a truly holistic digital twin that can be controlled to get the targeted performance.