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

Abstract

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.

Citation

Yan, J. (2020), "3D printing optimization algorithm based on back-propagation neural network", Journal of Engineering, Design and Technology, Vol. 18 No. 5, pp. 1223-1230. https://doi.org/10.1108/JEDT-12-2019-0342