Control System Design Of FDM 3D Printer And Process Parameter Optimization

As a digital emerging manufacturing process,it has received attention from various industries.In theory,3D printing technology can achieve all-round manufacturing,low production cost,rich and diverse printing consumables,and can accurately replicate printing models,which has a very ideal development prospect.The author first systematically analyzed and studied the fused deposition molding process,not only the technical requirements but also the working principle.On the basis of,the author designed the electrical principle and software and hardware implementation scheme of the FDM 3D printer control system.The first to be designed is the electrical principle and electronic circuit of the system.Secondly,the author completed the design of the core motor control planning algorithm.The temperature control system is designed using PID algorithm,and automatic parameter correction function is proposed.The man-machine interaction system based on IPS capacitive screen was designed,and the control system has the function of previewing the local print model.Two data storage units of SD card and U disk are designed to realize offline printing.Aiming at the shortcomings of 3D printing equipment that needs to be reprinted after power off,a function module for resuming printing after power off is designed.The author verified that the designed system has higher reliability and better accuracy through the comprehensive debugging experiment of the prototype.Aiming at the problem that the accuracy of parts is difficult to predict and the process control parameters are difficult to choose in the process of fused deposition molding,the author proposed a precision prediction model for FDM3 D printing molded parts,the author proposes a precision prediction model for fused deposition molding parts that is designed by combining GA algorithm and BP neural network.Combined with actual engineering experience,after analyzing the technical principle of fused deposition molding,the main influencing parameters of molding accuracy are established,the dimensional error and surface roughness are selected as the accuracy indicators of the part.The author obtained sample data through experiments,then apply sample data to train the GA-BP algorithm,use the same data to train the traditional BP algorithm.By analyzing the results,GA-BP prediction model has better prediction accuracy than BP prediction model.Aiming at the problem that mathematical programming method can not be used to solve the GA-BP prediction model,a process parameter optimization model based on particle swarm optimization is established.The parameters output by the model are used in actual printing.By analyzing the results,the size error and surface roughness of the actual printed product are very ideal.In other words,it can effectively improve the printing quality of fused deposition molding3 D printing by using the GA-BP-PSO algorithm combination proposed in this article.