Development of Hybrid Artificial Neural Network–Particle Swarm Optimization Model and Comparison of Genetic and Particle Swarm Algorithms for Optimization of Machining Fixture Layout

In this research paper, a methodology is proposed by combining taguchi’s parametric design, hybrid artificial neural network–particle swarm optimization (ANN–PSO) and evolutionary techniques to optimize the fixture layout by minimizing the maximum workpiece deformation on a 2D fixture workpiece system in end milling operation. Taguchi’s parametric design with five levels is utilized iteratively to estimate the potential range to place the fixture elements around the workpiece using the data obtained from finite element method. The hybrid ANN–PSO model is developed to predict the maximum workpiece deformation within the potential range in which PSO is utilized to optimize the weights and biases of the network. The diversity of data used for training the model is ensured by combining the experimental conditions of central composite design and Box Behnken design of response surface methodology. The developed model is tested using root mean square error, which exhibited better prediction accuracy. The hybrid ANN–PSO model is then optimized by genetic algorithm (GA) and PSO. The results clearly indicate that the PSO is capable of producing better fixture layouts with 0.1936% of superiority in solution quality than GA. Hence, the proposed approach is more viable to design the improved fixture layout with huge reduction in time and computational complexity.