基于Simulink反馈方法的钛合金铣削刀具磨损预测
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摘要
钛合金因其突出的综合性能广泛应用于航空航天领域,然而由于其难加工性,在钛合金切削过程中极易产生刀具磨损,所带来的加工精度低、表面质量差等现象严重影响钛合金的切削效率及经济效益。因此建立合理的钛合金铣削磨损模型对实际加工生产具有重大的指导意义。考虑了实际切削加工过程中的热力耦合条件,基于磨粒、黏结、扩散三种磨损机理,结合磨损过程铣削刀具的几何形状改变,利用Simulink软件进行反馈仿真预测,获得后刀面磨损带曲线;并通过实际铣削试验进行刀具寿命试验,对仿真模型的可靠性进行验证。结果表明,该方法可以较好地预测刀具后刀面磨损带长度随时间的变化,整体预测误差在22%以下。该研究提高了刀具磨损带的计算精度,并为切削参数的合理选择提供理论基础。
Titanium alloy Ti6Al4V is widely used in the aviation and aerospace industry. However, this material is typically difficult-to-machine due to its intrinsic characteristics, such as poor thermal conductivity, high strength at elevated temperature, etc. A novel method is proposed to predict tool wear in machining of the titanium alloy. In this method, a thermo-mechanically-coupled tool wear model, consisting of abrasion, adhesion and diffusion mechanisms, is implemented in the Simulink software. The geometric features of a worn-tool are updated at different wear stages in the simulation. Tool wear prediction is conducted and validated by milling experiments under various milling conditions. The results show that the proposed method can well predict the extent of the tool flank wear, and the relative error between the experimental and the prediction results is within 22%. This study offers a guidance to the determination of tool life and optimization of process parameters in a machining process.
Titanium alloy Ti6Al4V is widely used in the aviation and aerospace industry. However, this material is typically difficult-to-machine due to its intrinsic characteristics, such as poor thermal conductivity, high strength at elevated temperature, etc. A novel method is proposed to predict tool wear in machining of the titanium alloy. In this method, a thermo-mechanically-coupled tool wear model, consisting of abrasion, adhesion and diffusion mechanisms, is implemented in the Simulink software. The geometric features of a worn-tool are updated at different wear stages in the simulation. Tool wear prediction is conducted and validated by milling experiments under various milling conditions. The results show that the proposed method can well predict the extent of the tool flank wear, and the relative error between the experimental and the prediction results is within 22%. This study offers a guidance to the determination of tool life and optimization of process parameters in a machining process.
引文
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[2]ZHU Zhaoju,SUN Jie,LI Jianfeng,et al.Investigation on the influence of tool wear upon chip morphology in end milling titanium alloy Ti6Al4V[J].International Journal of Advanced Manufacturing Technology,2016,83(9-12):1477-1485.
[3]BERMINGHAM M J,SIM W M,KENT D,et al.Tool life and wear mechanisms in laser assisted milling Ti-6Al-4V[J].Wear,2015,322-323:151-163.
[4]LI Hao,HE Gaiyun,QIN Xuda,et al.Tool wear and hole quality investigation in dry helical milling of Ti-6Al-4Valloy[J].International Journal of Advanced Manufacturing Technology,2014,71(5-8):1511-1523.
[5]USUI E,SHIRAKASHI T,KITAGAWA T.Analytical prediction of cutting tool wear[J].Wear,1984,100(1-3):129-151.
[6]TAKEYAMA H,MURATA R.Basic investigation of tool wear[J].Journal of Engineering for Industry,1963,85(1):33.
[7]LUO X,CHENG K,HOLT R,et al.Modelling flan wear of carbide tool insert in metal cutting[J].Wear,2005,259(7):1235-1240.
[8]PáLMAI Z.Proposal for a new theoretical model of the cutting tool's flank wear[J].Wear,2013,303(1-2):437-445.
[9]HUANG Yong,LIANG S Y.Modeling of CBN tool flank wear progression in finish hard turning[J].Journal of Manufacturing Science&Engineering,2004,126(1):98-106.
[10]WANG Chengdong,MING Weiwei,CHEN Ming.Milling tool’s flank wear prediction by temperature dependent wear mechanism determination when machining Inconel 182 overlays[J].Tribology International,2016,104:140-156.
[11]孙玉晶,孙杰,李剑峰.钛合金铣削加工刀具磨损有限元预测分析[J].机械工程学报,2016,52(5):193-201.SUN Yujing,SUN Jie,LI Jianfeng.Finite element analysis on prediction of tool wear in milling titanium[J].Journal of Mechanical Engineering,2016,52(5):193-201.
[12]ZHANG Song,LI Jianfeng,DENG Jianxin,et al.Investigation on diffusion wear during high-speed machining Ti-6Al-4V alloy with straight tungsten carbide tools[J].The International Journal of Advanced Manufacturing Technology,2009,44(1):17-25.
[13]?ZEL T,SIMA M,SRIVASTAVA A K,et al.Investigations on the effects of multi-layered coated inserts in machining Ti6Al4V alloy with experiments and finite element simulations[J].CIRP AnnalsManufacturing Technology,2010,59(1):77-82.
[14]JIANG Hua.A cobalt diffusion based model for predicting crater wear of carbide tools in machining titanium alloys[J].Journal of Engineering Materials&Technology,2005,127(1):136-144.
[15]HOU Yongfeng,ZHANG Dinghan,WU Baohai,et al.Milling force modeling of worn tool and tool flank wear recognition in end milling[J].IEEE/ASME Transactions on Mechatronics,2015,20(3):1024-1035.
[16]LU Xiaohong,WANG Furui,JIA Zhenyuan,et al.Amodified analytical cutting force prediction model under the tool flank wear effect in micro-milling nickel-based superalloy[J].International Journal of Advanced Manufacturing Technology,2017:1-8.
[17]YAN Sijie,ZHU Dahu,ZHUANG Kejia,et al.Modeling and analysis of coated tool temperature variation in dry milling of Inconel 718 turbine blade considering flank wear effect[J].Journal of Materials Processing Technology.2014,214(12):2985-3001.
[18]KANNATEY-ASIBU E.A Transport-diffusion equation in metal cutting and its application to analysis of the rate of flank wear[J].Journal of Sustainable Mining,1985,13(54):36-40.
[19]SUN Yujing,SUN Jie,LI Jianfeng,et al.An experimental investigation of the influence of cutting parameters on cutting temperature in milling Ti6Al4V by applying semi-artificial thermocouple[J].The International Journal of Advanced Manufacturing Technology,2014,70(5):765-773.
[20]KASIM M S,HARON C H C,GHANI J A,et al.Wear mechanism and notch wear location prediction model in ball nose end milling of Inconel 718[J].Wear,2013,302(1-2):1171-1179.