A generalized framework for predicting process robustness in the context of machining aero engine components

In the manufacturing of highly valuable parts, such as aero engine components, reliable and robust machining processes are a key aspect to ensure consistent part quality and profitability. In serial production environments, a variety of inherent uncertainties exist due to similar, but not identical machines, tools, materials, and process conditions. These can lead to variability in tool wear, cutting forces, or surface integrity, ultimately impacting product quality. Inherent uncertainties in the production system cannot be eliminated and are part of the reality of serial production. However, predictive models that account for these uncertainties can quantify process variability, enabling the optimization of machining strategies under uncertain conditions. In order to optimize machining strategies that prioritize process robustness over machining time, a sufficiently scalable metric is required. This paper introduces the concept of a robustness score for machining processes and presents a generalized framework for the prediction of the probability of process success across multiple critical variables by considering the specific limits of the machining process. The proposed approach combines the fundamentals of reliability analysis with machining simulation models and probabilistic methods. Such a robustness metric can be used as a guide for process optimization to enhance process profitability by focusing on process reliability.