Research On Thermal Error Modeling Of Machine Tools Based On Least Squares Support Vector Machine

The main content of this dissertation is the model of thermal errors for CNC machine tools via support vector machine (SVM). Based on fully understanding and deeply analyzing the current status of the research and application of the thermal error modeling and compensation technique for CNC machine tools, least square support vector machine (LS-SVM) modeling is proposed. Firstly, the principle of statistical learning theory (SLT) and support vector machine (SVM) are introduced. Secondly, the theory of LS-SVM is presented in detail. Lastly, describing the thermal error experiment of machine tools, the LS-SVM modeling is validated in Matlab.In chapter 1, the background and the significance of the research are stated. The research history and current situation of the thermal error modeling are also provided. After the description of the shortcoming of current thermal error modeling, the SVM modeling is advised and the main content of the dissertation is presented.In chapter 2, the principle of SLT and SVM is introduced. At first, basic theory of SLT including the VC dimension, the bound of extending and the principle of structural risk minimization (SRM) are presented. Then, the characteristic of support vector machine regression (SVR) and kernel are dissertated. In the end, the improving technique of SVM is discussed.In chapter 3, the theory of LS-SVM is introduced. The special property of LS-SVM is described firstly. The sparse LS-SVM and the robust LS-SVM are deeply analyzed. At last, the choice about parameter of LS-SVM modeling is discussed, and a new method of parameter choice based on grid search is proposed.In chapter 4, the experiment of temperature-thermal Errors of CNC machine tools is introduced. The experiment equipments include CNC lathe, the temperature measure system, and the laser CCD displacement sensors. The scheme and the data of this experiment are discussed in detail.In chapter 5, the whole process of LS-SVM thermal error modeling is described. Firstly, the optimization parameter of this modeling is selected by grid search technique. Secondly, LS-SVM thermal error modeling included initialized modeling, robust modeling and sparse modeling is established. Finally, the superiority of LS-SVM modeling is validated by the data analysis and the contrast of least square modeling.In chapter 6, the study contents and conclusion of the dissertation have been summarized, and the further research works have been forecasted.