中文摘要：

为提高产品加工过程中质量监测的智能化程度,在运用控制图描述质量波动的基础上,提出了一种基于融合特征约减的KPCA-SVM控制图分类方法。先通过蒙特卡洛模拟生成控制图数据集,提取统计特征和形状特征,并将其与原始特征相融合,运用核主成分分析对高维融合特征降维,再使用遗传算法优化支持向量机的参数。通过仿真实验,将降维前后、不同分类器的识别精度进行了比较,结果表明运用所提方法能够得到更好的识别效果。

英文摘要：

In order to improve the intelligence of quality monitoring in machining processes, thepaper proposed a control chart classification method based on fusion feature reduction and KPCA-SVM, on the basis of quality fluctuation which was described by control chart. Firstly, the MonteCarlo method was applied to generate the control chart data sets, statistical features and shape features were extracted to fuse with original features, then kernel principal component analysis was applied to reduce dimensionality of high dimensional fusion feature sets. Finally, genetic algorithm wasused to optimize parameters of SVM. Recognition accuracy were compared through the simulation experiments with the applications of dimensionality reduction and different classification models, the results demonstrate that the higher recognition accuracy may be achieved by using the proposed method.