The investigation of cemented carbide tool wear and failure monitoring technique in heavy-duty cutting process based on artificial intelligent

doi:10.16731/j.cnki.1671-3133.2018.10.018

Abstract

Abstract: 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 multi-sensor 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.

Key words: heavy-duty cutting, tool wear, condition monitoring, artificial intelligent

CLC Number:

TG84

Cui Youzheng, Zheng Minli, Zhang Hongjun, Liu Meng, Sun Chaoyu. The investigation of cemented carbide tool wear and failure monitoring technique in heavy-duty cutting process based on artificial intelligent[J]. Modern Manufacturing Engineering, 2018, 457(10): 113-118.

References

[1] CHENG Yaonan,LIU li,SUN Shouhui,et al.Influence of thermal load on mechanical property of cemented carbide material and heavy cemented carbide inserts life[J].Journal of Harbin Institute of Technology(New Series),2013,20(6):59-66.
[2] 程程,顾玉萍,赵佳梅,等.大型轴类锻件切削性及粗加工增效措施研究[J].工具技术,2010,44(11):67-70.
[3] 严复钢,何耿煌,刘献礼.极端重载条件下高效切削刀具技术[J].哈尔滨理工大学学报,2011,16(6):11-16.
[4] 张景利,何耿煌,李文涛,等.大型锻造件筒节切削加工性能及提高切削效率措施[J].机械工程师,2010(5):139-141.
[5] 高宏力.切削加工过程中刀具磨损的智能监测技术研究[D].成都:西南交通大学,2005.
[6] ADAM G Rehorn,JIANG Jin,PETER E Oban.State-of-the-art methods and resultsin tool condition monitoring:a review [J].Int J Adv Manuf Technol,2005(26):693-710.
[7] PRASAD B S,SARCAR M M M,BEN B S.Development of a system for monitoring tool condition using acousto-optic emission signal in face turning—an experimental approach[J].International Journal of Advanced Manufacturing Technology,2010,51(1/2/3/4):57-67.
[8] 申志刚.高速切削刀具磨损状态的智能监测技术研究[D].南京:南京航空航天大学,2009.
[9] LANZETTA M.A new flexible high-resolution vision sensor for tool condition monitoring[J].Journa119l of Materials Processing Technology,2001,119(1):73-82.
[10] 谢厚正,黄民.基于振动测试的数控机床刀具磨损监测方法[J].仪表技术与传感器,2013(2):73-75.
[11] WONG Y S,YUEN W K,LEE K S,et al.Machine vision monitoring of tool wear[J].Proceeding of SPIE-The International Society for Optical Engineering,2008(3518):17-24.
[12] PRASAD K N,RAMAMOORTHY B.Tool wear evaluation by stereo vision and prediction by artificial neural network[J].Journal of Materials Processing Technology,2010,112(1):43-52.
[13] RYABOV O,MORI K,KASASHIMA N.In-process direct monitoring method for milling tool failures using a laser sensor[J].CIRP Annals-Manufacturing Technology,2016,45(1):97-100.
[14] MATSUMURA Takashi,USUI Eiji.On-line tool wear compensation system in milling opertion[J].Technical Paper-Society of Manufacturing Engineers,2009(MR99-172):1-6.
[15] 揭景耀.智能刀具状态监测系统研究与进展[J].中国机械工程,1997(6):60-63.
[16] 胡秋.CIMS环境下刀具状态监测研究回顾与展望[J].机床与液压,2003(6):17-18.
[17] 刘晓明.刀具破损监测系统的研究与开发[D].成都:电子科技大学,2005.
[18] 李娜.基于人工智能的刀具切削状态的监控研究[D].兰州:兰州理工大学,2008.
[19] SR D E D,LISTER P M.On-line metal cutting tool condition monitoring.I:force and vibration analyses[J].International Journal of Machine Tools & Manufacture,2010,40(5):739-768.
[20] CHOUDHURY S K,RATH S.In-process tool wear estimation in milling using cutting force model[J].Journal of Materials Processing Technology,2000,99(1/2/3):113-119.
[21] SAGLAM H ,UUNVAR A.Tool condition monitoring in milling based on cutting forces by a neural network[J].International Journal of Production Research,2003,41(7):1519-1532.
[22] SHAMOTO E,HINO R,UMESAKI M,et al.Intelligent Recognition of End Milling Conditions Based on Cutting Force Model(Development of Monitoring Method without Any Databases)[J].Transactions of the Japan Society of Mechanical Engineers C,2003,69(683):1927-1932.
[23] LI X.A brief review: acoustic emission method for tool wear monitoring during turning[J].International Journal of Machine Tools & Manufacture,2002,42(2):157-165.
[24] INASAKI I.Application of acoustic emission sensor for monitoring machining processes[J].Ultrasonics,2008,36(1/2/3/4/5):273-281.
[25] QIANG S,CHENG S,JING K.Tool wear intelligence measure in cutting process based on HMM[J].Applied Mechanics & Materials,2011(52/53/54):482-487.
[26] LI X.A brief review: acoustic emission method for tool wear monitoring during turning[J].International Journal of Machine Tools & Manufacture,2002,42(2):157-165.
[27] LEI M,YANG X,YANG S.Tool Wear Length Estimation with a Self-Learning Fuzzy Inference Algorithm in Finish Milling[J].International Journal of Advanced Manufacturing Technology,2009,15(8):537-545.
[28] JESU＇S R R D,GILBERTO H R.Driver current analysis for sensorless tool breakage monitoring of CNC milling machines[J].International Journal of Machine Tools & Manufacture,2003,43(15):1529-1534.
[29] 王军平,敬忠良,王安.基于随机模糊神经网络的刀具磨损量软测量技术[J].信息与控制,2002,31(6):534-537.
[30] MAN-LI L V,SUN L F.Multi-sensor Information Fusion Technology[J].Techniques of Automation & Applications,2008,10(6):428-432.
[31] SHENG Z Q,TANG C P,FAN H T,et al.Application of Multi-Sensor Information Fusion Technology in Machine-Tool Monitoring[J].Applied Mechanics & Materials,2011(88/89):714-717.
[32] MENG L W,GAO J P,XIN M Y,et al.Application of Multi-sensor Information Fusion Technology on Fault Diagnosis of Electrical System[J].ITM Web of Conferences,2017(11).
[33] GARRT O’ Donnell,PUAL Young,KEVIN Kelly, et al.Towards the improvement of tool condition monitoring system in the manufacturing environment[J].Journal of Materials Processing Technology,2014(119):133-139.
[34] JANTUNEN E,JOKINEN H.Automated on-line diagnosis of cutting tool condition[J].International Journal of Flexible Automation and Integrated Manufacturing,2016,4(3/4):273-287.
[35] HUTTON D V,HU F.Aeoustic emission monitoring of tool wear in end-milling using time-domain averaging[J].Journal of Manufacturing Seience and Engineering,2016,121(1): 8-12.
[36] 刘献礼,刘强,岳彩旭,等.切削过程中的智能技术[J].机械工程学报,2017,6(49):1-17.
[37] 陈道全,刘伯兴,吴家奎,等.重型切削加工刀具失效监控系统的开发[J].装备制造技术,2015( 8):83-86.