Optimization Of Warpage Deformation In FDM By Artificial Neural Network

Fused Deposition Molding (FDM) is the most widely used at home and abroad rapid prototyping technologies. It can quickly design from the CAD model into a prototype of a certain feature or part. However, the prevalence of FDM parts warpage problem, it directly affects the quality of forming parts. In the FDM process, the influence of process parameters warpage are more reasonable parameter settings can reduce the warpage parts, it is necessary to study the FDM process parameters optimization. Because these parameters and the forming quality of the complex between the nonlinear, strong coupling, resulting in various process parameters on the forming quality is difficult to use accurate mathematical model that is reasonably set the corresponding parameters will be very different complex. Therefore, this article FDM forming around the key influencing parameters, warpage prediction model and optimization of process parameters, etc. to carry out a more in-depth study.Warpage on the impact of various process parameters in-depth analysis, MEM-200 combines the characteristics of rapid prototyping machine to determine the impact of warpage of the five major process parameters, the main advantage of orthogonal factors were studied for the warping deformation of the neural network modeling study to provide the corresponding samples. The structure of neural network design, the use of Bayesian regularization algorithm to improve the generalization ability of BP network, through analysis and comparison to determine the optimal network structure to increase the forming time warping deformation and predictive ability. And based on the use of optimization algorithms, programming to meet the manufacturing requirements to achieve the optimization of process parameters, by forming the effect of test verification, the results show that the method is feasible.Therefore, further analysis of this study is the impact of warpage of FDM in the law laid the foundation for the follow-up process under the conditions of different equipment, model, optimization of process parameters to achieve a technical basis.