Prediction of Surface Roughness Based on Least Square Support Vector Machine in Low-frequency Vibration Cutting Process
首发时间:2014-03-31
Abstract:In this paper, a prediction method based on least square support vector machine is introduced into the surface roughness prediction model in low-frequency vibration cutting. The model is based on low-frequency vibration cutting experiment, to obtain the corresponding relation between vibration parameters and cutting parameters and the workpiece surface roughness. Construct the training sample set to train regression models of least square support vector machine through experimental data. Identify training sample set to solve the regression parameters a and b. The amplitude of A, vibration frequency f, feeding f1 and spindle speed n as the input variable Xi, obtain predicted values of surface roughness by applying forecasting model; using the value, which equals to the difference between predicted value and its actual measurements value of surface roughness, to evaluate forecasting model. Through examples, and compared with BP neural network and support vector machine method, obtain the following conclusion: under the condition of the same sample, based on least square support vector machine prediction model constructs an order of magnitude faster than SVM method, and the model prediction error is about 29% to support vector machine, the prediction accuracy is an order of magnitude higher than the BP model.
keywords: Low-frequency vibration cutting Least square support vector machine Prediction Model, Roughness
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基于最小二乘支持向量机的低频振动切削加工表面粗糙度的预测
摘要:本文将一种基于最小二乘支持向量机的预测方法引入低频振动切削加工表面粗糙度的预测模型。该模型基于低频振动切削试验,获取振动参数和切削参数与工件表面粗糙度的对应关系;利用试验数据构造最小二乘支持向量机的回归模型训练样本集、对训练样本集进行辨识,求解回归参数a和b值。以振幅A、振动频率f、进给量f1与主轴转速n为输入变量xi,运行预测模型获得加工表面粗糙度的预测值;并将此值与其实际表面粗糙度的测量值进行比对,求解得工件预测误差用以评价预测模型。通过实例,并与BP神经网络和支持向量机方法对比,得出如下结论:在相同的样本条件下,基于最小二乘支持向量机的预测模型构造速度比支持向量机方法高1个数量级,模型预测误差约为支持向量机方法的29%,预测精度比BP模型高1个数量级。
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No.4590872653997139****
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