【作者】 钟利民； 杨京； 程建春； 刘翔雄；

【Author】 ZHONG Li-ming;YANG Jing;CHENG Jian-chun;LIU Xiang-xiong;Institute of Acoustic, Nanjing University;Kunshan Hiecise Machine Co.,Ltd;

【机构】 南京大学声学研究所； 昆山华辰机械制造有限公司；

【摘要】 在机械加工过程中磨削砂轮的钝化状态对磨削效率和加工质量有着重要的影响,如何能够在不停止磨削进程的情况下自动识别砂轮的钝化状态,以判断是否需要对砂轮进行修整,将对提高加工效率和加工质量具有重要意义。本文提出了一种利用决策回归树(CART)算法来识别砂轮钝化状态的方法。对磨削过程中产生的声发射信号使用小波分析进行多尺度分解,提取各层小波能量将其作为砂轮钝化状态所对应的多个特征,然后通过CART算法构建出一个多分类决策树,对砂轮所处的各种钝化状态实现了精确分类。更多还原

【Abstract】 In the process of machinery, the dull state of grinding wheels is importance to the grinding efficiency and quality. The acoustic emission generated in the grinding process by wheels has a close relationship with its own material of wheels and the grinding state. Through analysis of acoustic emission, the dull state of the wheels can be clear in the course of grinding. In this article, a CART algorithm is proposed to recognize the dull state of the grinding wheels. The acoustic emission generated in the grinding process is multi-scale decomposed with the Wavelet Analysis. The various dull states of the wheels are represented by the distribution of wavelet energy. A decision tree classifier for multi-classification is built by CART algorithm. Thus, the various dull states of the wheels get classified.更多还原