Precision Model of Predicting FDM Rapid Prototype Based on BP Neural Network

G.Q. Shang; C.H. Sun; X.F. Chen; J.H. Du

doi:10.4028/www.scientific.net/KEM.392-394.891

Paper Titles

A Simulation-Based Methodology for Lot-Size Optimization
p.866

Research and Development on Individual Virtual Intelligent Vehicles
p.873

Finite Element Analysis of the Effects of Coated Tool on High Speed Orthogonal Machining
p.879

Research on Integrated System of Information and Function Based on Virtual Prototyping Technology
p.884

Precision Model of Predicting FDM Rapid Prototype Based on BP Neural Network
p.891

Research on the Service-Oriented Price Quotation Method and System for Rapid Prototyping
p.898

Research for Data Exchange Technology of Heterogeneous Database Based on XML
p.903

Mechanism Analysis of Ultrasonic Machining Based on Finite Element Method
p.908

CPLD Application in the Development of the Pin Selection Controller in Knitting Machine
p.913

HomeKey Engineering MaterialsKey Engineering Materials Vols. 392-394Precision Model of Predicting FDM Rapid Prototype...

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.