Predicting Product Precision in Fused Deposition Modeling Based on Artificial Neural Network

摘要

Technological parameters in fused deposition modeling are coupled and the forming process is a non-linear process. A large number of modeling parameters affect the quality of the product precision in fused deposition modeling. The BP (Back propagation) neural network prediction model of product precision is build. The neural network arithmetic is designed and the samples are acquired by fused deposition modeling experiment. The training samples are used to train the network to accomplish the mapping relation between input and output of the network. The test samples are used to verify the performance of the trained network. Simulation results indicate that the prediction model has sufficient accuracy. The BP neural network prediction model of product precision is feasible and valid in theory and in practice. The product precision BP neural network prediction model lays the foundation for real-time prediction in fused deposition modeling process, and it has great significance for improving the quality of formed parts.