Melt Differential 3D Printing Slicing Program Design And Molding Accuracy Optimization And Prediction

The melt differential 3D printing forming technology is a rapid prototyping technology based on the principle of melt deposition(FDM)molding.Based on the concept of polymer calculus,the nozzles stack layers of material into 3D solids according to the layered slice G-CODE information.This technology can meet the printing requirements of rigid polymer materials and soft elastic materials,and can also directly add polymer pellets.It breaks through the limitations of traditional FDM technologies in terms of material types and morphologies.Currently,it is a new type of 3D forming technology with great potential for development.However,because it's screw feed method and needle valve control method are different from the traditional fused deposition molding,there is no slicing software that fully meets the requirements in the market.When using them,you need to manually delete a large amount of code.At the same time,the molding process is affected by the nonlinear interaction of multiple factors such as the screw speed,all of which have great influence on the precision of plastic parts.At present,no in-depth study has been conducted on these issues.Therefore,it is a very meaningful task to carry out the design of slicing program and the analysis and optimization of forming accuracy for the melt differential 3D technology.The main research contents of this paper include the following four aspects:(1)The structure and control system of the melt differential 3D printing equipment were analyzed.It innovatively used micro screw type plastic feeding method and switch needle valve control method to realize rigid or elastic plastic pellets printing on demand.And through the comparison with the control method of the traditional fused deposition,the necessity of the slicing procedure was derived,and the device provided a platform for the follow-up experiments.(2)A slicing program for melt differential 3D printing equipment was developed.The structure of STL file was analyzed,which was a the commonly used input format of 3D printing data processing software.The analysis and design of the core algorithm such as the bottom layered slicing,the generation of the model outer wall,the generation of the model surface and filling outline,the trajectory generation of the filling area,and the path planning were completed.The object-oriented programming method was used to implement the entire data conversion function from the STL format to the final processing G-CODE.(3)The processing G-CODE obtained from the slicing program designed in this paper was tested,and an orthogonal test was designed to study the influence of key process parameters of the melt differential 3D printer on the accuracy of the product.The influences of process parameters such as nozzle temperature,layer thickness,print speed,screw speed,and printing pitch on the dimensional accuracy and surface accuracy of the product were obtained.And through the range analysis,the best combination of printing parameters was obtained.Finally,the experiment was verified and the optimization effect was obvious.(4)A method for predicting the accuracy of melt differential 3D printed products based on LM-BP neural network algorithm was proposed.The five main influencing factors influencing the accuracy of the products in the orthogonal test were used as input parameters of the model,with dimensional accuracy and surface accuracy as output targets.Based on the minimum prediction error,the number of neurons in the middle layer was determined to determine the structure of the BP neural network.The experimental results of multiple groups of orthogonal experiments were used as data samples for training.Thus,a BP neural network model was established.Through further experiments,the prediction effect of the model was verified,which could guide the application well and provide the basis for further optimization of forming accuracy.