## 1 Introduction  
Uncertainty is omnipresent in forming processes and is reflected in fluctuating product properties. Fluctuating influences from the semi-finished product, the tool, the machine and the environment prevent reliable manufacturing of components. For this reason, scientists have been researching approaches that contribute to the control of uncertainty for several years. Self-optimizing machine tools [1] and closed-loop control of product properties [2] are seen as promising higher-level approaches. Both approaches have in common that information about product properties of previous production steps is used to manipulate the product properties of subsequent workpieces. For this purpose, models are often used that draw on relationships between the control variables of the process and the product properties. In most cases, the relationships have high complexities, interdependencies and non-linearities, cannot be modelled by analytical models or are unsuitable for control due to long computing times [3]. Therefore, machine learning algorithms are increasingly used that allow a data-driven description of the relationship and whose model parameters are fitted in such a way that manually parameterized cost functions are minimized. Reinforcement Learning (RL) algorithms are particularly suitable for deriving optimal control variables in forming processes. Using the example of a Finite-Element Method (FEM) deep drawing simulation, [PERSON] et al. [4] show that at discrete time steps, the blankholder force can be optimized with respect to sheet thinning, cracking and stress peaks. [PERSON] et al. [5] show that coupling a RL algorithm with an analytical model in a rolling process leads to pass schedules that optimize both the mechanical properties of the components and the energy efficiency of the process. A similar approach is presented by [PERSON] et al. [6] in a multi-stage open-die forging process, where the RL algorithm is rewarded for low geometry deviations of the components as well as for a high utilization of the available press force. [PERSON] et al. [7] demonstrate the suitability of RL algorithms for controlling the sheet thickness in sheet metal strip rolling by adjusting the roll forces and tilts, resulting in reductions in sheet thickness variations. The first applications of RL algorithms for the design and control of forming processes can be found in the literature. However, their application is largely limited to simulative investigations. This is due to implementation barriers that make it difficult to transfer policies generated from simulation data to real processes. Reasons for this include sim-to-real gaps [8] that often cannot be closed due to a lack of availability of experimental data and latencies between reference variables and manipulated variables that are not simulated [9]. Thus, the full potential of RL for forming process has not yet been exploited. Therefore, in this paper, we present an approach for how RL agents trained on FEM simulation data can be transferred to experimental operation and contribute to the control of product properties. As an example process, a flexible die bending process is used that allows the production of different bending angles and materials. On the one hand, the process is modelled using an FEM simulation, which is used to generate synthetic data, and on the other hand, a flexible die bending tool is designed and manufactured to carry out the experimental tests. The paper is organized as follows. Section 2 introduces the basic principles of the die bending process and RL algorithms, which are fundamental for the understanding of the paper. Section 3 presents the methodology for RL-based control of the die bending process. The subject of the section is the setup of the FEM simulation, the experimental setup and the procedure for the application and training of the applied RL algorithm. Section 4 first presents simulative results on the performance of the RL algorithm under different levels of uncertainty and then its performance in the real process.