In order to improve the process of micron wood fiber cutting

an improved particle swarm algorithm and BP neural network based on the combination of optimization algorithm is proposed to achieve the precision machining of micron wood fiber. The error back propagation algorithm is used to achieve the best structure selection of the complex relationship between cutting parameters. The improved particle swarm optimization algorithm (PSO) solves the defect of local minimum convergence of BP network

and gives a scientific and reasonable output of cutting parameters. The precision and effectiveness of the precision training of the algorithm are verified by the simulation and optimization experiments of the cutting parameters of different tree species. The research shows that the improved optimization algorithm proposed in this paper can predict the cutting parameters of the wood to be processed and has a high training precision.