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

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