Analysing the Fatigue Behaviour and Residual Stress Relaxation of Gradient Nano-Structured 316L Steel Subjected to the Shot Peening via Deep Learning Approach

In this study, the effect of kinetic energy of the shot peening process on microstructure, mechanical properties, residual stress, fatigue behavior and residual stress relaxation under fatigue loading of AISI 316L stainless steel were investigated to figure out the mechanisms of fatigue crack initiation and failure. Varieties of experiments were applied to obtain the results including microstructural observations, measurements of hardness, roughness, induced residual stress and residual stress relaxation as well as axial fatigue test. Then deep learning approach through neural networks was used for modelling of mechanical properties and fatigue behavior of shot peened material. Comprehensive parametric analyses were performed to survey the effects of different key parameters. Afterward, according to the results of neural network analysis, further experiments were performed to optimize and experimentally validate the desirable parameters. Based on the obtained results the favorable range of shot peening coverage regarding improved mechanical properties and fatigue behavior was identified as no more than 1750% considering Almen intensity of 21 A (0.001 inch).