A Substructure-Informed Surrogate Framework for Uncertainty-Based Low-Cycle Fatigue Reliability Assessment of Integrated Blisk–Shaft Rotors

Integrated blisk–shaft rotors are susceptible to low-cycle fatigue (LCF) under complex operating conditions and parameter uncertainties, rendering direct finite-element-based probabilistic assessment computationally demanding. This study develops a surrogate-assisted fatigue-reliability framework integrating dominant-cycle identification, an improved cat swarm optimization-assisted artificial neural network (ICSO-ANN), and Monte Carlo simulation. Elastoplastic finite-element analysis is first performed to identify the fatigue-critical region and dominant fatigue cycle. Subsequently, 1000 finite-element samples are generated using Latin hypercube sampling. Four independent assessments are conducted, each using 200 samples for model development and 50 held-out samples for independent testing. The ICSO-ANN achieves a MAPE of 0.77%, demonstrating high accuracy in fatigue life prediction. Probabilistic analysis against a normally distributed design life requirement with a mean of 11,000 cycles yields a reliability of approximately 98.6%. On an identical 103-sample basis, the surrogate-assisted analysis achieves an approximately 69-fold computational speedup over direct finite-element-based Monte Carlo analysis. Sensitivity analysis further identifies fatigue strength parameters and rotational speed as the dominant contributors to fatigue life uncertainty. The proposed framework provides a computationally efficient approach for the uncertainty propagation and probabilistic LCF reliability assessment of integrated blisk–shaft rotors.