数控车削刀具磨损监测与预测技术  

Monitoring and Prediction Technology for Wear of CNC Turning Tools

作　　者：程寅 Cheng Yin(Fujian Meizhou Bay Vocational and Technical School,Putian,Fujian 351254,CHN)

机构地区：[1]福建省湄洲湾职业技术学校,福建莆田351254

出　　处：《模具制造》2024年第12期140-142,共3页Die & Mould Manufacture

摘　　要：随着制造业的快速发展,数控车削刀具磨损已成为影响加工质量和效率的关键因素。基于数字孪生技术,提出一种数控车削刀具磨损监测与预测技术,实现刀具磨损状态的实时监测与提前预警。通过构建刀具数字孪生模型,利用传感器网络实时采集切削过程中的振动信号、切削力信号等多源数据,采用神经网络技术进行特征提取与降维处理,建立高精度的刀具磨损预测模型。该技术能够准确预测刀具磨损趋势,及时发现潜在磨损问题,为刀具更换和维护提供科学依据,有效保障加工质量和提高生产效率。With the rapid development of manufacturing industry,the wear of CNC turning tools has become a key factor affecting machining quality and efficiency.Based on digital twin technology,a CNC turning tool wear monitoring and prediction technology is proposed to achieve real-time monitoring and early warning of tool wear status.By constructing a digital twin model of cutting tools,utilizing sensor networks to collect real-time vibration signals,cutting force signals,and other multi-source data during the cutting process,and using neural network technology for feature extraction and dimensionality reduction,a high-precision tool wear prediction model is established.This technology can accurately predict the trend of tool wear,timely detect potential wear problems,provide scientific basis for tool replacement and maintenance,effectively ensure machining quality and improve production efficiency.

关 键 词：数控车削 刀具磨损 监测与预测 数字孪生 

分 类 号：TG659[金属学及工艺—金属切削加工及机床]