Optimization of lapping process parameters of CP-Ti based on PSO with mutation and BPNN

This work aims to improve the surface quality of commercially pure titanium (CP-Ti) with free alumina lapping fluid and establish the relationship between the main process parameters of lapping and roughness. On this basis, the optimal process parameters were searched by performing particle swarm optimization with mutation. First, free alumina lapping fluid was used to perform an L-9(3(3)) orthogonal experiment on CP-Ti to acquire data samples to train the neural network. At the same time, a BP neural network was created to fit the nonlinear functional relation among the lapping pressure P, spindle speed n, slurry flow Q and roughness Ra. Then, the range of the neuron numbers in the hidden layer of the neural network was determined by empirical formulas and the Kolmogorov theorem. On this basis, particle swarm optimization with mutation was used to search for the optimal process parameter configurations for lapping CP-Ti. The optimal process parameter configurations were used in the neural network to calculate the prediction value. Finally, the accuracy of the prediction was verified experimentally. The optimum process parameter configurations found by particle swarm optimization were as follows: the lapping pressure was 5 kPa, spindle speed was 60 r center dot min(-1) and slurry flow was 50 ml center dot min(-1). Then, the configurations were applied to a neural network to simulate prediction: the roughness was 0.1127 mu m. The roughness obtained by experiments was 0.1134 mu m. The error was 0.62%, which indicates that the well-trained neural network can achieve a good prediction when experimental data are missing. Applying the particle swarm optimization (PSO) algorithm with mutation to a neural network will obtain the optimal process parameter configurations, which can effectively improve the surface quality of CP-Ti lapped with free abrasive.

Automated summary

The study aimed to improve the surface quality of CP-Ti by using free alumina lapping fluid and neural networks, finding optimal process parameters through particle swarm optimization with mutation. Experimental verification showed that the well-trained neural network provided accurate predictions for roughness when experimental data were lacking. Applying the PSO algorithm with mutation to a neural network successfully obtained the optimal process parameter configurations and improved surface quality effectively.