On-line Monitoring of Tool Wear Conditions in Machining Processes Based on Machine Tool Data

Abstract

Abstract: To realize the on-line monitoring of tool wear conditions, and improve the feasibility of monitoring system in machining processes, an on-lime cutting tool condition monitoring method was proposed based on machine tool data in machining processes. OPC UA was used for NC machine tool data acquisition and storing, and the internal machine process informations related to tool wear conditions were collected. Based on the process informations and corresponding wear informations, convolutional neural network was used to establish a recognition model of tool wear conditions. The performance of proposed method was verified in machining cases, and compared with other tool wear condition monitoring methods. This method is more suitable for tool wear condition monitoring in practical machining processes.

Key words: tool wear, object linking and embedding for process control unified architecture(OPC UA), on-line monitoring, convolutional neural network

摘要： 为实现刀具磨损状态的在线监测，提高监测系统的实用性，提出一种基于机床信息的加工过程刀具磨损状态在线监测方法。采用OPC UA通信技术在线采集与存储数控机床信息，得到与磨损相关的机床内部过程信息，并基于这类信息与相应的刀具磨损信息，利用卷积神经网络建立了刀具磨损状态识别模型。应用案例证明了该方法的监测性能，与其他传统监测方法相比，该方法更适用于实际的生产加工。

关键词: 刀具磨损, 用于过程控制的对象链接与嵌入统一架构, 在线监测, 卷积神经网络

CLC Number:

TP164

LU Zhiyuan, MA Pengfei, XIAO Jianglin, WANG Meiqing, TANG Xiaoqing. On-line Monitoring of Tool Wear Conditions in Machining Processes Based on Machine Tool Data[J]. China Mechanical Engineering.

卢志远, 马鹏飞, 肖江林, 王美清, 唐晓青. [设备健康管理]基于机床信息的加工过程刀具磨损状态在线监测[J]. 中国机械工程.

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