铣削表面粗糙度在线智能预测方法研究

On-line Intelligent Prediction Method for Milling Surface Roughness

为了提高表面粗糙度在线预测模型的精度,研究并提出了一种融合传感器统计学数据的表面粗糙度在线智能预测方法。该方法对加速度的统计学特征进行PCA主成分提取,保留了85%的数据信息。通过改进的PO-GRNN广义神经网络对训练集数据进行分配,确定光滑因子σ的近似最优值。随后结合铣削加工参数集与PCA主成分,通过PO-GRNN构建了一套在线粗糙度预测模型。纵向与横向对比实验结果表明:该模型可提供较高的粗糙度在线预测精度,能适用于当前智能制造过程中粗糙度的在线预测。

In order to improve the accuracy of the on-line prediction model of surface roughness, a method of on-line surface roughness prediction based on the statistical data of sensors is proposed. The PCA princi- pal component analysis was used to extract the statistical characteristics of acceleration, and 85 % of the data information was retained. An improved PO-GRNN generalized neural network is used to allocate the training set data to determine the approximate optimal value of the smoothing factor or. Then, based on the milling parameters and PCA principal components, a set of on-line roughness prediction model was constructed by PO-GRNN. The experimental results show that this model can provide a high precision of on-line roughness prediction and can be applied to online prediction of roughness in current intelligent manufacturing process.