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Forming tools for deep drawing of car body parts have large dimensions and complex free-form surfaces. Deep drawing tools are manufactured by means of milling in multiple steps. The loads of the forming process cause a considerable deviation of the tool geometry or the surface quality from the initial state after several forming operations are carried out. The tools are reworked when the deviation becomes unacceptable. The surface quality is assessed by the subjective experience of the worker and through spot check measurements. An unambiguous and automatized assessment of the tool surface based on reliable surface parameters has not been realized so far. An approach is to predict the surface parameters already on the basis of the finishing process parameters. This approach will help to develop a methodology for the production and rework of deep drawing tools. The superordinate goal is the online compensation of surface parameter deviations during the milling process. Therefore, an accurate prediction of the idealized surface quality is necessary. Within this task, a multivariate regression expression for 3d surface parameters Sa, Sz and Sq in dependence on the milling parameters elliptic ball diameter, angle of milling tool axis, feed, infeed, angle of the milling lanes, surface convexityand surface dimensions is to be derived. First, a surface scatter topography with variable resolution is to be calculated as a function of the given milling and surface parameters. The scatter generation is to be conducted using the programming language Python. The generated surface is to be visualized in a 3d plot. Subsequently, the 3d surface parameters are to be implemented for the calculation on an adequately discretized surface scatter or on an analytic function of the surface topography. Using the model of the surface, an analysis on the effect of the variation of milling and surface parameters over industrial parameters is to be conducted. The analysis leads to multivariate regression expressions for the 3d surface parameters in a last step. The result of the work is a scripted tool which enables to predict the idealized surface quality of ball-milled surfaces and a sensitivity analysis of the ball-milling parameters on different surface shapes. The thesis is to be thoroughly documented in written form. The following tasks are to be solved: • Implementation of a script to generate surface topographies in dependence on milling and surface parameters • Implementation of a script to visualize the generated surface topographies • Implementation of a script to derive surface quality parameters from a generated surface topography • Analysis of the effect of milling parameters on the surface quality • Development of a multivariate regression expression for the 3d surface parameters Sa, Sz and Sq