Fatigue prediction and optimization of laser peened turbine blade using artificial neural networks and ANFIS

Abstract

AbstractThis paper investigates the fatigue behavior prediction of Ti‐6Al‐4V thin‐leading‐edge turbine blade specimens treated with laser shock peening (LSP) using two advanced artificial intelligence (AI) methods: artificial neural networks (ANNs) and adaptive network‐based fuzzy inference system (ANFIS). The study aims to estimate the endurance under high cycle loading conditions. First, using ABAQUS and MATLAB software, the modified Crossland criterion for uniaxial loading is applied to recalibrate endurance limit values based on modifications induced by the LSP process. Then, these techniques are employed to predict the modified Crossland criterion profile and endurance limit values influenced by the LSP treatment. Specifically, numerical values are used as training and testing data for these AI models. As a result, these AI methods provide highly accurate prediction and optimization of the modified Crossland criterion and endurance limits, demonstrating their reliability and effectiveness.