The clinching process in sheet metal production is affected by variations caused by material and production factors such as batch inconsistencies, non-uniform preconsolidation of sheet materials, and tool wear. These variations lead to fluctuations in important quality parameters such as undercut, neck, and bottom thickness, resulting in inconsistent joint quality. These inconsistencies can negatively impact the load-bearing capacity of a clinched joint. The research aims to enhance process monitoring by incorporating the recording of geometric joint characteristics. This will be achieved by using a rapid in-situ simulation during the joining process to determine the resulting quality parameters of the joint. A quick calculation model based on linear-elastic FEA will be developed and coupled with a semi-analytical approach to correct nodal displacements, thereby minimizing deformation energy in the system. This approach significantly reduces calculation time, as only a linear-elastic “forward” calculation is performed, without iterative loops. This novel process monitoring will enable medium-term control of clinching processes, laying the foundation for self-learning processes and intelligent process control.