Research On Subdivision Driving And Temperature Prediction Of 3D Forming System

At present,the three-dimensional molding technology is accepted by people around the world,and the technologyit of digital model file quickly into biological materials or photosensitive resin is developing rapidly,promising prospects.Desktop level of three-dimensional molding machines have a material widely,low equipment requirement,convenient operation and so on.But the hardware of three-dimensional molding machine,mainly includes the motor,molding nozzle,the host controller and so on,its internal structure is complex,the stable operation of the machine and the accurate control of temperature are important links to ensure the forming accuracy.Therefore,how to improve the forming precision of the three-dimensional molding machine research has the vital significance.In this paper,Independent research and development of the three-dimensional molding machine is researched,first,the whole system and hardware structure of three-dimensional molding machine are designed,system software and hardware platform and part of the internal resources in the application of three-dimensional molding machine are majorly researched,and the whole work of 3d molding system chain and control principle are given.Then analysis of stepping motor driven system,including the concept,characteristics and principle of subdivided driving technology are focused on.In addition,in view of the stepper motor stepper motor step or insufficient leapfrog,and easy to produce resonance,In this paper,Subdivided driving method of current vector of The constant amplitude current vector even rotation is put forward,the method using ideas of maintaining current synthetic vector amplitude constant,changing evenly rotating angle to make the constant amplitude current vector and even subdivided driving.Verified by simulation experiment,and compared with commonly used subdivided driving method,the method in the application of three-dimensional molding system is feasible,and not only can make the system five axis motor coordination and stable operation,but also forming precision is improved.Secondly,molding nozzle temperature directly affects the forming of the molten material physical effect.For three-dimensional molding machine,molding nozzle temperature fluctuations in molding effect even the influence of nozzle lifetime,BP neural network based on quantum particle swarm optimization is put forward to establish molding nozzle temperature prediction model,the method adopting the optimization thinking complement each other,using the quantum particle swarm optimization to the BP neural network's initial weights and thresholds,to predict the temperature trend in order to achieve the purpose of precise control of temperature.Matlab simulation proves that the application of the control effect of the algorithm.Finally,in terms of software design and experimental test,the three-dimensional modeling software and slicing software used in the forming process are analyzed,and the control interface is designed.After forming the upper and lower position machine debugging,in the self-developed platform of actual processing hunter head material,the two methods proposed in this paper is verified by experiment.Test and analysis show that the optimized molding material than before optimization of roughness was reduced by 0.3 mm.The proposed method can provide reference for fine processing of forming system control.