The integration of additive manufacturing processes, such as wire-based Directed Energy Deposition, with subtractive manufacturing processes into a hybrid-additive manufacturing chain within a robotic cell presents a promising solution to reduce resource consumption in production. However, robotic systems, due to their lower stiffness compared to machine tools, face challenges in machining additively manufactured components. Unstable surface structures and the microstructure of additively manufactured components result in higher milling forces and instabilities, affecting component quality in terms of shape deviations and surface quality. This work aims to reduce the static and dynamic compliance of the robotic kinematics during the milling process through the reorientation of the TCP, leading to a more precise and stable machining process. In preliminary tests, additively manufactured components are characterized, and their machinability is examined, revealing higher milling forces and unstable processes compared to conventional components. Subsequently, a dynamic analysis of the robot structure is conducted using modal analyses and operational vibration measurements. It is observed that chattering occurs due to mode coupling, and the change in TCP orientation has an impact on dynamic behavior. Favorable orientations related to dynamic behavior are identified, leading to more stable machining processes. Following this, the static behavior of the robot structure is examined, and the stiffness model of the robot structure is created using the extended forward kinematics modeling approach. The optimal experimental setup for identifying stiffness parameters is determined through Bayesian inference, reducing the required number of experiments. Based on the stiffness model, an optimization model is then constructed to optimize the robot's pose, maintaining the cartesian position while minimizing the displacement of the TCP under a defined load. The optimization calculates the optimal axis position of the robot system to minimize displacement in the specified spatial direction or all spatial directions. Finally, the insights from the optimization and dynamic analysis are validated in machining experiments on additively manufactured components. The use of optimized orientations demonstrates the best results in terms of dimensional accuracy and surface quality. Even orientations improved for dynamic stability achieve lower shape deviations than the initial orientation. The study concludes that reorienting the TCP can make the milling process more accurate and stable. The approach offers a cost-effective and easily implementable way to improve the accuracy and stability of machining additively manufactured components. These findings contribute to integrating the robotic-based hybrid-additive manufacturing chain into production and advancing the transition to a flexible and resource-efficient manufacturing process.