摘要
重型切削硬质合金刀具磨/破损严重将导致其加工质量、生产效率的降低,以及生产成本的增加,因此对刀具的磨损状态监测就显得尤为重要。对近年来在学术期刊上发表的关于刀具磨损在线监测研究文献中采用的方法和技术作了简要的回顾,对整个智能刀具磨损监测系统作出了详细的分析。通过比较各种方法的优缺点,提出了多传感器信息融合技术将作为智能刀具磨损监测技术的主要研究方向。总结了目前刀具磨损监测系统存在的问题,并提出了相应的解决思路。通过借鉴普通切削加工刀具磨/破损监测技术,在重型切削过程中引入刀具磨/破损智能监测方法,可以解决以往重型切削需要通过预先频繁更换刀片的方式来保证工件加工质量的问题,在重型切削领域具有重要的应用价值和发展前景。
The cemented carbide tool wear and failure monitoring in heavy-duty cutting process is so serious, the state of cutting tool has important practical significance to ensure the quality of processing, reduce the production cost and improve the production efficiency. So the monitoring condition of tool wear is particularly important. A summary of the monitoring signals for tool wear and failure monitoring in metal cutting that have been tested and reported in the literature was presented. The intelligent tool monitoring system is analyzed in detail. By comparing the advantages and disadvantages of various methods ,the hybrid intelligent multisensor information fusion technology will be the main direction of the future development of intelligent tool wear monitoring technology. The existing problems of tool wear monitoring system are summarized, and the corresponding solutions are put forward. By drawing on the common cutting tool grinding and damage monitoring technology, the tool wear and damage intelligent monitoring methods are introduced in the heavy cutting process, which solves the problem that the former heavy cutting needs to replace the tool blade frequently in advance to ensure the quality of the workpiece. It has important application value and development prospects in heavy-duty cutting field.
作者
崔有正
郑敏利
张洪军
刘萌
孙超宇
Cui Youzhengj,;Zheng Minli;Zhang Hongjun;Liu Meng;Sun Chaoyu(School of Mechanical and Electronic Engineering,Qiqihar University,Qiqihar 161006,Heilongjiang,China;The Key Lab of National and Local United Engineering for "High-Efficiency Cutting & Tools",Harbin University of Science and Technology,Harbin 150080,China;School of Mechatronic Engineering,Lingnan Normal University,Zhanjiang 524048,Guangdong,China;Second Affiliated Hospital of Qiqihar Medical College,Qiqihar 161006,Heilongjiang,China)
出处
《现代制造工程》
CSCD
北大核心
2018年第10期113-118,共6页
Modern Manufacturing Engineering
基金
国家自然科学基金资助项目(51575146)
黑龙江省省属高等学校基本科研业务费科研资助项目(135209308)
黑龙江省省属高等学校基本科研业务费科研资助项目(135109307)
黑龙江省教育科学“十三五”规划2017年度备案课题-省青年专项课题(GBD1317145)
关键词
重型切削
刀具磨损
状态监测
人工智能
heavy-duty cutting
tool wear
condition monitoring
artificial intelligent