支持向量机刀具磨损预测模型及MATLAB仿真

针对刀具使用时加工参数多变的实际情况，提出使用最小二乘支持向量机（LS—SVM）建立模型并对刀具磨损进行预测：首先引入最小二乘支持向量机建立刀具磨损模型，然后针对具体实验数据，采用交叉验证的办法，选取优化的核参数。实验和仿真结果表明：该模型可以有效地学习刀具磨损中的非线性关系，刀具磨损的预测精度较高。因此该模型可以用作对实际加工中的刀具磨损进行有效预测，并为切削参数的实际选择提供依据。

Predicting Model of Cutting Tool Wear Based on Least Squares Support Vector Machine and MATLAB Simulation

With the variable parameter appearing in the using of tool,the model built by least squares support vector machines(LS-SVM)method is provided to predicting tool wear.Firstly,LS-SVM was introduced to model the wearing of tool.Aiming at the specific sample,the method of cross validation is used to choose the proper kernel function parameters.Results of simulations and experiments showed that the LS-SVM model based on radial basis function kernel(RBF) could effectively learn the non-linear relationship in tool...