Comprehensive optimization of shot peening intensity using a hybrid model with AI-based techniques via Almen tests

Abstract Shot peening is a crucial surface treatment technique that significantly improves the mechanical properties of metallic components, particularly their fatigue resistance and ability to withstand corrosion cracking. This study aims to optimize the shot peening process for aviation applications by evaluating and comparing various mathematical modeling and optimization techniques. Seven mathematical models were analyzed using a neuro-regression method (NRM), among which the second-order trigonometric non-linear (SOTN) model exhibited the highest reliability, achieving R 2 values of 0.93 and 0.90 for training and testing datasets, respectively. To improve the model’s robustness, four optimization algorithms – differential evolution (DE), simulated annealing (SA), Nelder–Mead (NM), and random search (RS) – were applied to the SOTN model. Although each technique offered valuable insights, performance fluctuations across different intensity ranges necessitated the development of a hybrid optimization model that combines the strengths of all four methods. The hybrid model achieved a mean error of approximately 2.69 %, outperforming individual approaches and demonstrating strong potential for reliable shot peening optimization across a wide range of target intensities. These findings provide a comprehensive methodology for AI-based optimization of surface treatment processes in engineering applications.