Abstract:For the problems of molding parts accuracy is difficult to control and high cost of parameter optimization experiment in selective laser sintering process, a method of using the seeker optimization algorithm to optimize the BP neural network to predict the accuracy of molding parts was proposed. Firstly, five process parameters of laser power, preheating temperature, scanning speed, scanning distance and layer thickness were selected to design orthogonal experiments for sample data. Then, the search strategy was determined according to the four behaviors of self-interest, altruism, pre-action and uncertain reasoning unique to the SOA algorithm, and the optimal weight and threshold of the BP neural network were obtained. Finally, using MATLAB to establish an optimized BP neural network prediction model to predict and analyze the sample data with comparing the prediction results with the traditional BP neural network and the BP neural network optimized by particle swarm optimization. The results show that the BP neural network prediction model based on SOA has high prediction accuracy, and the maximum absolute error is only 0.028 which has a guiding effect on the improvement of the accuracy of SLS molded parts and the selection of process parameters.