Paper Title:
Precision Model of Predicting FDM Rapid Prototype Based on BP Neural Network
  Abstract

Fused deposition modeling (FDM) has been widely applied in complex parts manufacturing and rapid tooling and so on. The precision of prototype was affected by many factors during FDM, so it is difficult to depict the process using a precise mathematical model. A novel approach for establishing a BP neural network model to predict FDM prototype precision was proposed in this paper. Firstly, based on analyzing effect of each factor on prototyping precision, some key parameters were confirmed to be feature parameters of BP neural networks. Then, the dimensional numbers of input layer and middle hidden layer were confirmed according to practical conditions, and therefore the model structure was fixed. Finally, the structure was trained by a great lot of experimental data, a model of BP neural network to predict precision of FDM prototype was constituted. The results show that the error can be controlled within 10%, which possesses excellent capability of predicting precision.

  Info
Periodical
Key Engineering Materials (Volumes 392-394)
Edited by
Guanglin Wang, Huifeng Wang and Jun Liu
Pages
891-897
DOI
10.4028/www.scientific.net/KEM.392-394.891
Citation
G.Q. Shang, C.H. Sun, X.F. Chen, J.H. Du, "Precision Model of Predicting FDM Rapid Prototype Based on BP Neural Network", Key Engineering Materials, Vols. 392-394, pp. 891-897, 2009
Online since
October 2008
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Price
$32.00
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