Research On Processing Parameters Optimization And Application For Wedm

As an important way of special machining,wire electrical discharge machining(WEDM)is widely used in mold processing,automobile production,military industry,aerospace industry and other mechanical fields.Nowadays,there are problems such as the backwardness of information technology and the lack of process database in WEDM companies,which have limited the development of WEDM industry.In view of this situation,under the background of “Made in China 2025”,cloud service model was applied to the field of WEDM,and the concept of cloud service platform for WEDM was proposed.In this dissertation,cloud service platform for WEDM was constructed inspired by the concept of cloud manufacturing.Cloud service platform for WEDM consists of resource layer,virtual resource layer,service running tool layer,application layer,management layer,and client.Through the cooperation of all layers,cloud service platform for WEDM can provide users with future-oriented intelligent services.Based on the cloud service platform,remote monitoring system and cloud process database for WEDM were built using mainstream technology.Remote monitoring system enables remote monitoring of WEDM machine real-time status using a mobile phone and other terminals.And cloud process database enables distributed storage,efficient sharing,and convenient management of WEDM process data.To deal with the contradictional requirements that obtaining both a better surface quality and a faster processing speed at the same time in reciprocated travelling wire EDM,from the complex nonlinear relationship between the processing parameters and process indicators such as surface roughness(Ra)and material removal rate(MRR),a series of experiments were designed to analyze in detail the effects of processing parameters on process indicators.On the basis of the experimental results,response surface methodology(RSM)and particle swarm algorithm(PSO)were applied to acquire the optimized parameters combination by establishing a multiobjective model.The results showed that the multi-objective optimization model is reliable and efficient in reducing Ra and increasing MRR.