Lithium-ion batteries are considered a cornerstone of electrochemical energy storage technologies. Ever since their commercialization by Sony in 1991, research has consistently pushed the state of the art of these systems. Over the years, researchers have shifted some of their focus from solid-state physics and chemistry to understanding the architecture and texture of the electrodes . Such a strategic shift is necessitated by consumer demand for safer and more energy dense batteries at a lower cost. Particularly safety concerns, such as the flammability of Li-ion batteries and further degradation effects are a major factor that can discourage their use in automotive technologies and thus limit their widespread adoption. Advanced high-performance battery chemistries, that mitigate the inherent shortcomings of traditional Li-ion batteries, as a successor to the current state of the art is therefore important. Solid-state batteries, that replace the liquid electrolyte for ionic conductivity by a solid electrolyte (SE), are considered an important candidate for this, as they allow for higher theoretical energy densities. One of the challenges impeding the widespread commercialization of solid-state batteries is the complexity arising from their electrodes manufacturing. Such manufacturing processes should ensure optimal interfaces between the active material and solid electrolyte to ensure electrodes with electronic and ionic conductivities. Optimized solid-state battery manufacturing processes are of paramount importance to allow for a low cost adaptation. Different types of battery manufacturing technologies are currently under development to achieve this in a cost-effective manner. On the laboratory-scale, cathode materials need to be laboriously ground and mixed. . An emerging alternative is a solvent-free manufacturing approach, in which, a molten binder, active material and solid electrolyte are extruded at high temperatures. Such process have already been successfully digitalized by us recently by using a discrete element method approach. Extrusion, however, is a technology that is not yet mature enough for electrode mass production. Roll-to-roll methods, such as wet processing, are already wide-spread for the manufacturing of Lithium-ion batteries. In wet processing, a slurry, containing the electrode components and a solvent, is tape-casted onto a current collector (CC) and dried. Once the solvent has completely evaporated, the resulting electrode is calendered to a desired thickness. These production methods have already been optimized for Lithium-ion batteries and benefit from economies of scale for low-cost production. Similar processes can be used for solidstate battery manufacturing. However, replacing the liquid electrolyte with a SE introduces other disadvantages in terms of outcome performance. Particularly, since the electrolyte is no longer able to wet the active material efficiently, interfaces are created between different materials, which induces capacity fade and mechanical instabilities thus reducing ionic conductivity on account of sluggish interfacial kinetics. Understanding the processability of solid electrolytes is also essential to developing more optimized electrode microstructure and understanding the interfacial evolution during process-ing and operation is important to maintain contact at the interface and thus ensure a continued ionic conductivity. In this regard, understanding the microstructural evolution of electrodes during manufacturing and the impact of manufacturing parameters on the electrochemical performance is critical. Researchers often use nondestructive means, such as, computer-generated tomography imaging to understand the internal microstructure. This approach is time consuming and is often unable to accurately distinguish all the material components. Most segmentation methods involve separating structures by their gray scale in the nano-CT image. If similar gray scales are present, different phases cannot be uniquely determined by segmentation processes. , which presents difficult in analyzing microstructures. Regarding this, computational methods can be used to gain insights into the electrode microstructure. Historically, microstructures were simply randomly generated and in fact, most computational electrochemical studies are performed on stochastically generated structures. Particles corresponding to active material (AM), SE, and mixtures of binder and carbon black (Carbon-Binder or CB), were randomly placed into a predefined volume to numerically study the impact of, for example, particle distributions on the electrochemical performance. However, experimentally, on both the laboratory and industrial scales the method of producing the battery electrode influences the end-result, morphologically and thus electrochemically. Coupling the microstructure to the manufacturing parameters enables a more systematic approach to understanding the electrochemistry. In response to lab and industry demand, computational models were developed to study the microstructure and by the ERC-ARTISTIC project to link the manufacturing process to the electrochemical performance of batteries. In the ARTISTIC models, a combination of physics based and machine learning models were developed as a digital twin of the manufacturing process chain. These models began with the simulation of Nickel Manganese Cobalt oxide (NMC)-based cathodes for lithiumion batteries and were later expanded to include other battery chemistries such as all solid-state batteries. Such models generally consist of performing a coarsegrained molecular dynamics or discrete element method simulation of each step in the electrode processing line, followed by Lattice Boltzmann Method and Finite Element Method simulations of the electrolyte filling and the resulting electrochemical behavior. A slurry is generated by mixing the electrode constituents, then dried by solvent removal and finally calendered. A refinement of the model was later performed by our group to account for microstructural evolution at different drying rates. The force fields were adapted in order to account for capillary forces between particles. A continuum approach was later introduced by Zihrul et al. by implicitly modeling capillary, buoyancy, friction forces and binder migration as well as the effects of drying on layer thickness through the convection-diffusion equation. A pure continuum approach to electrode drying has also been proposed by Susarla and Nikpour. Recently coupled fluid dynamics and discrete element method simulations of fluid-structure interactions have emerged to simulate a coarse model of the drying process. These models use a volume of fluid approach with a volumetric source term to study the multiphase nature of solvent evaporation, however do not consider the distribution of the inactive binder phase. Approaches to calendering simulations have previously been developed and recently further extended by our group to involve deformation and fracture of realistic particle morphologies upon electrode calendering. For solid-state battery manufacturing, computational generation of microstructure is often limited to the stochastic generation of microstructures. This is a computationally economical approach to study the influence of the microstructure on the electrochemical properties, which does not take into account the complex effect of manufacturing on the microstructure. Alabdali et al. , from our group, developed the first model linking the microstructure of a solid-state battery to its manufacturing process, by taking into account spherical shapes for the active material. As a proof-of-concept, a further development of the existing simulation method of the solid-state battery manufacturing line with wet processing, developed by Alabdali et al. is reported in this article. The novelty of the work reported here lies in the extraction of realistic AM particle shapes from nano-CT images and using these in the solid-state battery electrode manufacturing simulation workflow, unlike in previous work from our group, in which purely spherical particles were used. Previously, the inclusion of realistic shapes was only done for Li-ion batteries. This addition in the simulations allows for a more accurate determination of electrochemical properties of the solid-state battery electrode. Including such realistic shapes allows for scientists to improve their understanding of the actual paths electrons and ions are able to take through the microstructure and allows for the determination of more realistic interface properties. To the best of our knowledge, computational workflows considering realistic particle shapes have never been considered for solid-state batteries. As a proof-of-concept, our aim is to present an approach with existing data that works with limited computational resources. Limitations and possible extensions of our model are detailed. This article begins by introducing the theory and methodology. Here, the simulation framework is described and the process used to represent realistic shapes is explained. Subsequently, attained results are analyzed and discussed. Finally, conclusions are drawn and perspectives of our work are presented.