A fatigue life prediction framework of laser-directed energy deposition Ti-6Al-4V based on physics-informed neural network

Purpose Pores are the primary cause of fatigue failure in laser-directed energy deposition (L-DED) titanium alloys, which are largely determined by their location, size and shape. It is crucial for promoting the application of L-DED titanium alloys and ensuring their safety that establishing a fatigue life prediction method induced by pores, resulting in a proposed fatigue life prediction framework for L-DED Ti-6Al-4V based on a physics-informed neural network (PINN) algorithm. Design/methodology/approach In this study, a novel fatigue life prediction framework for L-DED Ti-6Al-4V based on a PINN algorithm was proposed. The influence patterns of various fatigue-sensitive parameters were revealed. The paper also included validation and analysis of the method, such as hyperparameter analysis of the PINN, efficacy analysis driven by physical information and comparative analysis of different methods. Findings The proposed method demonstrated high accuracy, with a correlation coefficient of 0.99 with experimental life. The coefficient of determination was 0.95 and the mean squared error was 0.06. Originality/value The results indicate that the proposed fatigue life prediction framework was of strong generalization capability and robustness.