Abstract
Evaluating the service conditions of high pressure turbine blades in areoengine, which is the key point for component design and remaining life prediction, has always been a great challenge. In this study, we report a method, which is developed by back propagation artificial neural networks (BPANN), for service condition evaluation of turbine blades by establishing a quantitative correlation between the microstructural evolution and the temperature, stress and time. This method was successfully applied to a directionally solidified superalloy DZ125 based on microstructural datasets obtained from temperature-stress-time (T-σ-t) simulations. As an example, the service condition of a turbine blade made of DZ125 superalloy were evaluated by the BPANN model using the microstructural descriptors, including γ′ volume fraction (Vf), γ′ rafting degree (Ω) and thickness of the rafted γ′ precipitates (D), and service time. This study shows great potential in accurately assessing the degradation and predicting the remaining life for hot section components made from directionally-solidified and single crystal nickel-based superalloys.
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