ISSN:
1013-9826
Source:
Scientific.Net: Materials Science & Technology / Trans Tech Publications Archiv 1984-2008
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
Fused deposition modeling (FDM) has been widely applied in complex partsmanufacturing and rapid tooling and so on. The precision of prototype was affected by many factorsduring FDM, so it is difficult to depict the process using a precise mathematical model. A novelapproach for establishing a BP neural network model to predict FDM prototype precision wasproposed 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, thedimensional numbers of input layer and middle hidden layer were confirmed according to practicalconditions, and therefore the model structure was fixed. Finally, the structure was trained by a greatlot of experimental data, a model of BP neural network to predict precision of FDM prototype wasconstituted. The results show that the error can be controlled within 10%, which possesses excellentcapability of predicting precision
Type of Medium:
Electronic Resource
URL:
http://www.tib-hannover.de/fulltexts/2011/0528/01/57/transtech_doi~10.4028%252Fwww.scientific.net%252FKEM.392-394.891.pdf
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