Yan, J (2020) 3D printing optimization algorithm based on back-propagation neural network. Journal of Engineering, Design and Technology, 18(5), pp. 1223-1230. ISSN 1726-0531
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 3D printing; back-propagation neural network; dimensional precision; fused deposition modeling; surface precision |
| Index terms: | optimization algorithm, methodology, neural network, 3D printing, product model, modelling, propagation, validity, influencing factor |
| Subjects: | analytical methods, artificial intelligence, modelling and simulation, manufacturing, engineering process, evaluation and assessment methods, risk assessment, algorithms, research methods |
| Topics: | Digital Applications, Risk Management, Research Practice, Engineering Principles |
| Descriptive scope: | 4 PCTA |
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