Optimization Of Process Parameters For Micro-milling Superalloy GH4169

With the continuous development of miniaturization and complication of parts,micro-milling methods have also received increasing attention.Since micro-machining is an emerging field,processing methods have not matured.The micro-cutting process is small in size and shows a size effect,and the macro-cutting theory is no longer applicable.Therefore,there is no reasonable and reliable basis for the selection of cutting parameters during machining.In actual processing,the data requirements for cutting force,cutting temperature,etc.are controlled within a reasonable range,which requires that the cutting parameters are predicted and optimized based on the target.Nickel-base superalloys have good corrosion and creep resistance,high strength,and are suitable for high stress strain and high temperature conditions.Widely used in aerospace and shipbuilding and other fields.Based on the above points of view,the following work was done in this thesis:(1)In order to explore the minimum cutting thickness of superalloy GH4169,an orthogonal model of two-dimensional micro-turning superalloy GH4169 was established using Abaqus software.The radius of the tool nose is set to 0.015 mm.Using the cutting thickness as a variable,5 sets of experiments were conducted to analyze the cutting force of the tool and the variation of the cutting temperature of the material during the cutting process of the GH4169 material.According to the results,with the increase of cutting thickness,the cutting force in the feed direction increases,and the cutting force in the vertical feed direction increases first and then decreases,and the maximum temperature of the workpiece first increases with the change in cutting depth.Lower and raise again.The first increase in cutting force and workpiece temperature is again inconsistent with the traditional cutting law,which is due to size effects.From these performance analyses it can be obtained that the minimum cutting thickness of the material is approximately 0.4r,ie 0.006 mm.(2)A three-dimensional micro-milling model was established.Three factors and five levels of orthogonal experiments were designed to investigate cutting force parameters,ie,cutting force,cutting temperature and residual,using cutting parameters such as spindle speed,feed per tooth,and depth of cut as factors.The effect of stress.The cutting force obtained in the experiment was mostly around 4N,the temperature was about 200 degrees,and the residual stress was mostly 1000 MPa.Similar to the data in the literature.Through the analysis of the results,the effect of the feed per tooth on the cutting force is the most significant.The depth of cut has the most significant effect on the workpiece temperature and residual stress.(3)Establish neural network-genetic algorithm predictive optimization model.Taking orthogonal experimental parameters as samples,cutting parameters as independent variables,cutting force,cutting temperature,and residual stress as dependent variables,the neural network is trained and the cutting parameters are input into a trained network,and the experimental results can be predicted.The prediction error of the cutting force parameter is maximum 26%,the maximum temperature prediction error is 61%,and the residual stress prediction error is 21%.Neural network prediction model has good prediction effect on cutting force and residual stress,and has poor prediction effect on workpiece temperature.After that,the genetic algorithm was applied to optimize the cutting parameters using the neural network as a fitness function.When the spindle speed is taken as 12201.6r/min(line speed 383.33mm/s),the feed per tooth is taken as 0.008mm/z,and the cutting depth is taken as 0.019 mm,the minimum cutting force is 0.014 N.When the spindle speed is 14463.6r/min(line speed 454.39mm/s),the feed per tooth is 0.013mm/z,and the cutting depth is 0.015 mm,the workpiece temperature is the lowest,which is 22.30°C.When the spindle speed is taken as 38418.7r/min(line speed 1206.96mm/s),the feed per tooth is taken as 0.011mm/z,and the depth of cut is taken as 0.016 mm,the residual stress is the minimum,which is 674 MPa.The above results are verified through experiments.The genetic algorithm is more accurate in the optimization of cutting parameters.