In this article a previously developed framework for the simulation of the wet-processing steps for solid-state battery electrode manufacturing was extended to account for realistic particle shapes. Simultaneously, the model was calibrated to experimental results, so that experimental conditions can be reproduced as closely as possible. The simulation is based on an alternative to stochastic generation of microstructures, which links each step of the manufacturing process to an experimentally validated property of the electrode. In particular, in our model, we studied the effect of calendering on the structure-property relations of the cathode. Our results matched the trends expected in experiments. In particular, we observed a homogeneous distribution of all materials throughout the microstructures. Interestingly, calendering appears to mostly have a scaling effect on the distribution of all phases within the electrode, because all material phases are compressed along the height of the electrode and the pore shrinkage during calendering. We expect such a behavior for slow drying rates, albeit models have been developed by us that can account for fast drying as well. Qualitatively, we were able to observe the pore shrinkage in our model as well. This effect occurred non-linearly with respect to the calendering degree for the microstructures after the springback effect. We also studied the evolution of interfaces between the different phases in our simulation. Here we observed a decrease in interfacial area for all materials and pores as we increase the calendering degree. This effect was the most pronounced for solid electrolyte and pores, because the volume of the solid electrolyte was the highest of all the materials. As a result of some of our assumptions, we were unable observe an increase in interfacial area between different materials on account of their spherical and rigid nature. Therefore, as we increase the calendering degree, we cannot increase the interfaces between two different materials beyond their initial contact area. Such trend is not observed experimentally and is con-sidered as a limitation of the model. Nevertheless, we observed an increase in electronic and ionic conductivity as we increase the calendering degree on account of the shorter percolation pathways of the charges. Although the model is suitable for microstructure prediction, it can be improved. The porosity of an electrode is a property that is averaged over a relatively large domain. Our simulations only cover several micrometers. Using the porosity as a validation metric can lead to results that do not consider implicit structural variations within the electrode microstructure. It is however sufficient for our case, because it captures the average value of the actual microstructure. Furthermore the methodology used to simulate drying of the slurry neglects the physical intricacies encountered during the drying of actual electrodes and can only be used to capture the effects encountered in the limit of low drying rates. A setback is also the use of non-overlapping spheres, which limits the processability of each material present in the microstructure. Future iterations of this model should consider these limitations. Although these disadvantages exist, this method of microstructure generation remains one of the most complete ways of linking manufacturing with the electrochemistry of the electrode. It constitutes a good compromise between high-fidelity electrode microstructure generation and computational cost. Future iterations of such models should model different formulations to observe the effects of different microstructures on the structure-property relations of the system. It might be fruitful to study larger systems for more representability. Furthermore, these models can serve to better understand the springback effect observed after the calendering roll is removed.