Research On Five Axis Coordinated Control And Temperature Prediction For 3D Molding Machine

As a new type of printing technology,3D molding machine is widely used in machinery manufacturing,medical equipment,jewellery and many other fields due to its simple operation,low cost,and the ability to print complex parts.However,the hardware and equipment modules of the 3D molding machine are so many that the internal structure is complex.Therefore,it is very important to improve the stability of the motor and the accuracy of the temperature for the hot sprinkler.With the research background of three dimensional forming system that it is the independent research and design of our school,the coordinated control of multiple motors and the temperature prediction of the hot sprinkler are studied on this experimental platform,which is used to improve the printing precision of the system.The main research contents are as follows:Firstly,the overall architecture scheme is researched on the existing experimental platform.According to its forming principle,the hardware structure,software application and mechanical design of the system are analyzed.Based on these foundations,two research points are extracted,which are coordination optimization between multiple motors and thermal nozzle temperature prediction feedback,and the design scheme is given from both hardware and software.Secondly,by establishing stepper motor and five-axis(X,Y,Z1,Z2,and P extruders)linkage models,the linkage deviation of the five-axis motor is analyzed and the optimal design idea is given.The Fuzzy Neural Network and PID algorithm are explored.While avoiding the shortcomings of these algorithms,their respective advantages are fully utilized and integrated.Five-axis forming optimization algorithm is proposed,which is based on Fuzzy Neural Network PID.Simulation and experimental results show that the stability of the five-axis motor is significantly improved,the deviation is reduced,and the adjustment time is shortened by 3.02s.Besides,a temperature prediction algorithm is proposed to solve the product forming effect of the thermal nozzle temperature,which is based on the Particle Swarm Optimization Support Vector Machine(PSO-SVM).Using PSO to optimize the SVM parameters,the advantages of the two algorithms are fully utilized to avoid shortcomings.They are the input quantities which are external environment of printing and the speed of thermal nozzle movement,the simulation prediction is performed through training samples.The experimental result shows that compared with the previous algorithm,the algorithm has higher prediction accuracy,the average relative deviation is reduced by 5.6%,the effect of the temperature control of the hot nozzle and molding accuracy are obviously meliorative.Finally,a description is very detailed about modeling,slicing,and molding software involved in the entire molding process.The control interface and each parameter value are designed,the software and hardware systems are debugged.The humanoid animated objects are processed on the optimized experimental platform.The experimental results show that the physical smoothness of the molding product is significantly improved after optimized by the method proposed in this paper,and the maximum roughness decreased by 0.42 mm.Thus,these methods can be used as a reference for the accurate control of the tremendous3D molding machine.