A Novel Method for Tool Wear Prediction in Titanium Milling by Simulink Feedback Method

doi:10.3901/JME.2019.11.224

Abstract

Abstract: 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.

Key words: milling, Simulink, titanium alloy, tool wear, wear model

CLC Number:

V261

DING Baoyang, BAI Qian, LIU Julong, ZHANG Bi. A Novel Method for Tool Wear Prediction in Titanium Milling by Simulink Feedback Method[J]. Journal of Mechanical Engineering, 2019, 55(11): 224-232.

References

[1] SHOKRANI A,DHOKIA V,NEWMAN S T. Environmentally conscious machining of difficult-to-machine materials with regard to cutting fluids[J]. International Journal of Machine Tools & Manufacture,2012,57(2):83-101.
[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-4V alloy[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] ÖZEL 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 Annals-Manufacturing 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. A modified 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.