Constrained probabilistic multi-objective optimization of shot peening process

Shot peening is a cold working process used for improving the fatigue strength of metallic components. An optimum set of peening parameters must increase the residual compressive stress (RCS), but reduce the surface roughness and cold work for improving the fatigue strength. The optimization is made robust to avoid any unfeasible solution that may arise out of random variations of input variables. The current study uses the well-known Design and Analysis of Computer Experiments (DACE) methodology for optimization, which is better than the conventional Design of Experiments (DoE) approach. It employs a finite element method based unit cell approach to determine the RCS, surface roughness and cold work of a given material. Radial basis functions are used to develop the meta-models. A genetic algorithm (GA) is employed for finding a robust and optimum set of shot peening parameters for a given material. With this approach, the operator will achieve the optimum solution specified by the designer.