Research On The Strategy Of High Precision Grinding For Hard Surface With Elastic Abrasives

Alloy M300 is widely used in the field like electronic,communication and aerospace industry because of its high hardness,high wear resistance and high dimension stability.With the ever-growing demands of good properties,long serving life and high reliability,followed by higher requirements for the machining precision and surface quality.However,due to the complex influence factors in the finishing of cemented carbide surface,process parameters selection mainly depends on the human's experience,which mainly results in low efficiency and a high rejection rate.The elastic abrasives is a good choice for overcoming the shortages of traditional machining technology in curved face grinding.While this method can reduce surface damage and concave-convex scratch,the complexity of contact feature between flexible abrasives and the surface may leads to irregular erosion of abrasive tools and different performance under the same parameters.Therefore,the contact feature is still a question that remain unaddressed until now.Aiming at the relationship between processing efficiency and contact characteristics in theory and improving the technology of finishing the curve surface,the concept of removal rate is introduced based on Hertz contact theory.Then the model of residual peak is established by analyzing the micro contact characteristics of the elastic abrasives.Through analysis of the model,the functional relationship between the residual peak removal area and the main parameters affecting the grinding accuracy is derived.The relationship can be concluded that a constant pressure on surface can ensure uniform removal of materials under certain technology parameters after analyzing the manual polishing process as well as Princeton formula.A L9(34)orthogonal testing is further conducted to obtain the influence degree of each parameter on the polishing effect and the best parameter combination in this model.In the end,the optimized process parameters is selected and the best parameter combination is determined by applying support vector machine(SVM).The correctness of these optimized parameters are verified by a set of experiments.