Prediction of irregularities and study of mechanical properties in laser shock peened nickel alloy

Nimonic 263, a high-performance Ni-based super alloy is known for its excellent resistance to creep, corrosion, and oxidation, in addition to surface stability at higher temperatures. The present study investigates the impact of Laser Shock Peening (LSP) on the microstructure and mechanical properties of Nimonic 263. Although LSP is a promising technique for extending the fatigue life of turbine components and other high-temperature applications, the choice of crucial parameters is important in order to avoid the negative implications including surface irregularities and reduced stress benefits. The research aims to analyze how different laser parameters, such as laser beam diameter and energy, affect surface compressive residual stresses, microhardness, surface roughness, and microstructure. Defect detection is also performed on the collected scanning electron microscopy (SEM) images using machine learning (ML) models. With an increase in energy density, there is an improvement in the microhardness and surface roughness. XGBoost is found to be the best-performing algorithm for defect prediction in the SEM images, among the ML models with an accuracy value of 0.95.