question: Abstract Despite the increasing digitalization of manufacturing processes in the context of Industry 4.0, the process design and development of machining processes poses major challenges for today’s manufacturing technology. Compared to the conventional process design, which is influenced by an empirical "trial-and-error" principle, the simulative process design offers the possibility of reducing development time and costs while at the same time improving the process understanding. A possible simulation technique to achieve these goals is the Finite Element Method (FEM). The FEM enables the calculation of the thermo-mechanical load spectrum underlying the machining process. Therefore, different input models are required. One of the most critical input models is the material model, which describes the constitutive material behavior. To determine the material model parameters, either (conventional) material tests, which require an extrapolation into the regime of metal cutting, or inverse techniques are used, where the process itself is used as a material test. Using the inverse technique, the model parameters are modified iteratively until a predefined agreement between simulations and experiments is achieved. The evaluation of the agreement bases on integral process variables, such as the cutting force, and their simulative counterparts. However, the procedure of the inverse determination requires high computational efforts and is not robust. This paper presents a novel approach to enhance the robustness of the inverse material model parameter determination from the cutting process. Orthogonal cutting tests on AISI 1045 steel have been conducted on a broaching machine tool over a range of different cutting speeds and undeformed chip thicknesses to set an experimental database. Thereby, the workpiece material was investigated in the two different heat treatments: normalized and coarse-grain annealed. The machining experiments showed differences in terms of the integral process results when comparing the two heat treatments. These results motivated for the development of a methodology capable to determine material model parameters robust and inversely from the machining process, which can be used with lower computational effort. To simulate the machining process, a Coupled-Eulerian-Lagrangian (CEL) model of the orthogonal cutting process has been set up. The material model parameters have been inversely determined using the Downhill-Simplex-Algorithm, which has been modified for this case. By using the Downhill-Simplex-Algorithm, it was possible to determine material model parameters within 17 iterations and achieving an average deviation between the experiment and the simulations below 10 %. Thereby, different process observables such as temperature, forces, and chip form have been used for the evaluation. Through this method, it is possible to determine material model parameters, which enable a good match between experiments and simulations with a low computational effort.