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Aiming at the problems of high cost of batch 3D printing and complex batch scheduling of multi-task and multi-machine 3D printing, an optimization model aiming at minimum average cost per unit volume was established, and an intelligent scheduling method with improved particle swarm optimization algorithm was proposed to solve the model. Firstly, the average cost per unit volume of 3D printing was built by analyzing the printing workshop and production process. Then, with the improved particle swarm optimization algorithm, the average cost per unit volume was taken as the fitness, and the scheduling sequence was taken as the location information of the particles. The solution of the problem was represented by two-dimensional coding in order, and the dynamic inertia factor of linear decreasing weight was applied to adjust the global and local searching ability. After the algorithm is iterated, the optimal value of the objective function and the corresponding solution set are obtained. The experimental results show that the average cost per unit volume of 3D printing can be reduced by 0.101 3GBP/cm3 compared with that of single printing process, and the total production cost can be effectively reduced and the utilization efficiency of 3D printer can be improved.