A Digital Twin Based Residual Stress Prediction Method for Turbine Blades

Abstract

The investment casting process is the conventional method for manufacturing turbine blades, and the residual stress generated through this process is widely regarded as a critical factor that markedly impacts the quality and serviceability of turbine blades. However, real-time measurement of residual stress is extremely complex and expensive, especially for single-crystal turbine blades. Current experimental measurement techniques such as X-ray or neutron diffraction can only measure residual stresses at specific points or within a very small area. Studies on residual stress of turbine blades are mainly performed by finite element (FE) simulation, but the accuracy is difficult to guarantee due to the inability to establish accurate mathematical models for complex physical systems. To address this issue, this paper proposes utilizing a digital twin (DT) model to predict residual stresses in turbine blades. By combining real-time sensor and FE simulation data, the DT model is able to update and correct the mathematical models, resulting in more accurate predictions compared to traditional FE simulations.