Computational roundness error prediction for internal turning operations

This work introduces a novel method for predicting roundness errors in internal turning processes using finite element simulations and custom computational routines. The approach is described and analyzed. Validation experiments are performed and demonstrate the effectiveness of the method in providing roundness estimates that align with real-world results. The approach provides a-priori insights into achievable tolerances and serves as a virtual data source for sensory clamping devices, enabling real-time monitoring during machining. The potential for future use in the context of a digital twin of turned workpieces is discussed, as well as the implications for future use in the context of a self-optimizing machining system.