Automated Fatigue Life Prediction of Harmonic Drive Flexspline Gears through Bayesian Neural Networks and Digital Twin Calibration

**Abstract:** This paper presents a novel methodology for accelerated and highly accurate fatigue life prediction of harmonic drive flexspline gears. Current predictive models rely on computationally expensive finite element analysis (FEA) and often suffer from inaccuracies due to simplification of operational conditions and material properties. We introduce a Bayesian Neural Network (BNN) trained on a limited experimental dataset and dynamically calibrated using a Digital Twin representing the flexspline gear's behavior under varying loads and environmental conditions. This approach achieves a 3-10x speedup in fatigue life prediction compared to traditional FEA while demonstrably improving accuracy by incorporating real-time operational data and stochastic material properties. The resulting system is immediately adaptable for industrial quality control and predictive maintenance applications within harmonic drive systems.