Study On Discharge State And Experimental Of WEDM Of Variable Thickness Workpiece In Gas

With the rapid development of my country's manufacturing industry,WEDM is widely used in the processing of many complex parts.Using the atmosphere as a processing medium,compared to processing in liquid and water mist media,it can achieve higher processing accuracy,and at the same time it is cleaner and greener,and has a greater development prospect.When cutting the workpiece with variable thickness,the sudden change in the thickness of the workpiece will seriously affect the gap discharge state and the surface consistency after processing.The stability of the discharge state is the guarantee for normal and reliable processing,so a reliable discharge state detection system is very important,it is of great significance for optimizing processing parameters and improving surface consistency after processing.Firstly,this article analyzes the machining mechanism of WEDM in the atmosphere,and obtains the discharge theory of WEDM in the atmosphere.Five different types of typical discharge state waveforms are comprehensively analyzed,and then each electrical signal waveform is processed accordingly.Getting the characteristic value of each discharge state,and using this as the judgment basis for each state.At the same time,according to the detected electrical signal of the gap,the identification strategy of the thickness of the workpiece is determined.Secondly,in terms of the classification and recognition of each discharge state,the electrical signal characteristic values obtained from the previous experimental research are used for training and learning of the ELM neural network,and then the five discharge states are identified and classified,and the proportion of each discharge state is obtained at the same time,which improves the classification accuracy rate of each discharge state is calculated.Then,in terms of system detection,by selecting appropriate components,using LABVIEW software and intelligent algorithms to cooperate with each other,the entire detection platform was built,so that the various discharge states during processing can be detected in real time,and parameter adjustment prompts have been added to the system.Modules make the functions of the system more perfect.Finally,the built detection system is applied to the cutting experiment.The experiment is mainly performed three times.The first time is the variable parameter rough cutting in the emulsion,so that each evaluation index meets the requirements of the first rough cutting;The second variable parameter semi-finishing process is performed in the fog to further reduce the surface roughness and obtain better surface consistency,making a transition for the third finishing process;finally,perform the finishing process in the atmospheric medium and pass the orthogonal test and comprehensive analysis,select the best parameter combination,and use the response surface analysis method to establish the model of each evaluation index,optimize a set of optimal fixed processing parameters through multi-objective,and compare them with the optimized three sets of variable parameters The test results show that machining variable thickness workpieces with variable parameters can obtain better surface roughness and consistency.