机械制造与自动化2026，Vol.55Issue(4)：39-43,5.DOI:10.19344/j.cnki.issn1671-5276.2026.04.008
基于声信号与SVM的机械加工表面粗糙度在线监测
Online Monitoring of Machined Surface Roughness Based on Acoustic Signal and SVM
摘要
Abstract
To address the need for online monitoring of surface roughness during hard turning,acoustic signals combined with a support vector machine(SVM)are employed for machining quality diagnosis,and principal component analysis(PCA)is applied to reduce the dimensionality of the acoustic features.The results show that the extracted first principal component is strongly correlated with material removal rate and surface roughness parameters,while the third principal component is closely related to cutting speed.Based on this correlation,an SVM classifier using the RBF kernel function is developed to identify three different levels of surface roughness.Under the optimal configuration,the method achieves a recognition accuracy of 100.0%.The study confirms that acoustic signals can serve as an effective tool for monitoring surface roughness during hard turning,providing a new solution for online quality monitoring in machining processes.关键词
表面粗糙度/声学监测/硬车削/PCA/SVMKey words
surface roughness/acoustic monitoring/hard turning/PCA/SVM分类
机械制造引用本文复制引用
马鸿..基于声信号与SVM的机械加工表面粗糙度在线监测[J].机械制造与自动化,2026,55(4):39-43,5.基金项目
陕西省教育厅科学研究计划项目(24JK0318) （24JK0318）
