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.