Research On Optimization Of Process Parameters Of Abrasive Water Jet Polishing

Abrasive water jet polishing technology is a new type of special processing method developed on the basis of abrasive water jet cutting technology.Compared with traditional mechanical polishing technology and many modern polishing techniques,abrasive water jet is the only cold working method.No tool wear,no pollution,small reaction force,high processing flexibility,the workpiece will not produce thermal deformation and other advantages.Moreover,the traditional polishing processing technology(mechanical polishing: grinding,etc.)in the processing of slender pipe,shaped surface and other complex parts,because of its polishing head can not be touched,resulting in difficult processing,or even can not achieve polishing.Therefore,it is necessary to study the abrasive water jet polishing technology.Based on the liquid-solid two-phase flow model and the fluid simulation mathematical model,the influence of the abrasive water jet polishing process parameters on the surface processing quality of the abrasive water jet erosion workpiece is discussed by simulation and experiment.Based on the traditional optimization method,the artificial intelligence algorithm is introduced to optimize the polishing parameters,and the best combination of the minimum surface roughness values is obtained and verified experimentally.The main research contents are as follows:(1)Study on the basic characteristics of abrasive water jet solid-liquid two-phase flow.Based on the theory of jet fluid,the small hole jet and the boundary layer theory,the basic characteristics of the solid-liquid two-phase flow of the abrasive water jet,the main characteristic parameters of the water jet and the main parameters of the abrasive water jet are studied.The effects of the water jet and abrasive jet were studied.The mechanism of material removal and the removal mechanism of abrasive water jet erosion and erosion were studied.(2)Simulation analysis of abrasive water jet erosion based on fluid simulation mathematical model.The numerical simulation of the external flow field of the water jet nozzle was carried out by fluent software.The flow characteristics of the external fluid in the water jet nozzle were studied.The velocity,volume,pressure and wall shear force of the pure water jet and abrasive water jet were compared.The influence of abrasive water jet erosion nozzle on the flow field inside and out of the nozzle was studied.The effects of variable angle,variable distance and abrasive water jet under variable pressure were studied.The distribution of the velocity,the pressure and the shear force of the wall outside the erosion nozzle,and the experimental results were carried out under the same conditions.(3)Effect of abrasive water jet polishing process parameters on processing quality.Based on the experiment of abrasive water jet polished steel plate,the effects of abrasive water jet polishing parameters(jet pressure,nozzle traverse speed,spray angle,target distance,abrasive flow rate and lateral feed rate)on the workpiece surface quality were discussed.The results show that the optimal combination of polishing parameters and the influence rate of each factor on the surface roughness are found.The surface roughness of the polished workpiece is predicted by the response surface regression analysis.And the interaction between the polishing parameters is discussed.Finally,the response surface of the polishing process parameters is solved.(4)Optimization of process parameters based on integrated ANN-GPS-SA abrasive water jet.Based on the BP neural network,the BP neural network prediction model of abrasive water jet polishing steel plate is established.The reliability of the prediction model is verified by experiments.Based on the response surface analysis and BP neural network prediction model,the artificial intelligence algorithm SA,simulated annealing algorithm,GPS and genetic model search algorithm),and then the parameters are optimized by the integrated ANN,SA and GPS(ANN-SA-GPS)technology,and the minimum surface roughness value Finally,the experimental results,regression prediction model results,SA,GPS and ANN-SA-GPS optimization results were evaluated and analyzed.Finally,the experimental results were analyzed and analyzed.