Reasearch On Cutting Parameter Optimization Based On Workpiece Surface Texture

With the development of automatization, the optimization of cutting parameters have become into the key of reducing production cost and increasing production efficientcy. Therefore, it is significant to study the method of optimizing the cutting parameters using all kinds of theories. The key to optimize parameters is how to select the optimal target. With the object of workpiece surface texture, this paper integrates the method of texture spectrum and the theory of Markov Random Field, lucubrates the selecting of optimal target and brings forward some new methods. The main research contents are as follows:Firstly, the paper talks about the significance and background of the research. The application of parameter optimization based on workpiece surface texture is analyzed. The advantages and disadvantages of texture analysis are presented.Secondly, this paper studies the methods of correlative analysis and texture spectrum, including their application theory, approach and the involved attentions. In order to recognize the smoothness and integrity of texture, this paper emphasizes the method of mended texture spectrum based on workpiece surface texture and brings forward the weighing target of smoothness and integrity.The third part is to research tool wear condition based on the Markov Random Field model. It introduces the theories of Vision Labeling and Markov Random Field. The Gauss Markov Random Field (GMRF) is established to analyze workpiece surface texture. Besides, this paper researches the parameter estimation methods, compares the effect between the least squares (LS) estimate and the maximum likelihood (ML) estimate and analyzes the inconsistency between the order of neighborhood and the speed of managing. The researched results showed the relative distance d based on the fifth order GMRF model can be used to recognize the tool wear condition...