TY  - JOUR
AB  - Purpose To obtain a high-quality finished product model, three-dimensional (3D) printing needs to be optimized.Design/methodology/approach Based on back-propagation neural network (BPNN), the particle swarm optimization (PSO) algorithm was improved for optimizing the parameters of BPNN, and then the model precision was predicted with the improved PSO-BPNN (IPSO-BPNN) taking nozzle temperature, etc. as the influencing factors.Findings It was found from the experimental results that the prediction results of IPSO-BPNN were closer to the actual values than BPNN and PSO-BPNN, and the prediction error was smaller; the average error of dimensional precision and surface precision was 6.03% and 6.54%, respectively, which suggested that it could provide a reliable guidance for 3D printing optimization.Originality/value The experimental results verify the validity of IPSO-BPNN in 3D printing precision prediction and make some contributions to the improvement of the precision of finished products and the realization of 3D printing optimization.
VL  - 18
IS  - 5
SN  - 1726-0531
DO  - 10.1108/JEDT-12-2019-0342
UR  - https://doi.org/10.1108/JEDT-12-2019-0342
AU  - Yan Jinshun
PY  - 2020
Y1  - 2020/01/01
TI  - 3D printing optimization algorithm based on back-propagation neural network
T2  - Journal of Engineering, Design and Technology
PB  - Emerald Publishing Limited
SP  - 1223
EP  - 1230
Y2  - 2024/09/21
ER  -