3D printing optimization algorithm based on back-propagation neural network

3D printing optimization algorithm based on back-propagation neural network PurposeTo obtain a high-quality finished product model, three-dimensional (3D) printing needs to be optimized.Design/methodology/approachBased 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.FindingsIt 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/valueThe 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Engineering, Design and Technology Emerald Publishing

3D printing optimization algorithm based on back-propagation neural network

Journal of Engineering, Design and Technology, Volume 18 (5): 8 – Mar 11, 2020

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1726-0531
DOI
10.1108/JEDT-12-2019-0342
Publisher site
See Article on Publisher Site

Abstract

PurposeTo obtain a high-quality finished product model, three-dimensional (3D) printing needs to be optimized.Design/methodology/approachBased 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.FindingsIt 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/valueThe 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.

Journal

Journal of Engineering, Design and TechnologyEmerald Publishing

Published: Mar 11, 2020

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