Particle Swarm Optimization approach for waterjet cavitation peening

摘要

An attempt has been made to solve water jet machining associated with cavitation peening problem through Particle Swarm Optimization (PSO) technique. The swarm initialization is started with Water pressure, stand off-distance and traverse speed. The fitness estimation of PSO is Residual stress, Hardness and Surface profile roughness. These dependent and independent parameters are used in water jet machining to induce beneficial residual stress into the surface layer. These parameters would adversely affect and greatly influenced hardness and surface finish of the machine samples. The position of each particle and conversion of the particle position to velocity are carried out on the PSO algorithm to find the appropriate optimum position of the particle. By following the above procedure, through the PSO algorithm, single order iteration equations are calculated for the independent parameter and it is found to have a correlation of 98% satisfactory limit with the experimental observations. With the developed PSO model, a minimum number of experimental runs will be sufficient to resolve any machining problems that are associated with optimization. (C) 2019 Elsevier Ltd. All rights reserved.