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Investigation of tool flank wear effect on system stability prediction in the milling of Ti-6AI-4 V thin-walled workpiece

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Abstract

Excessive tool wear can shorten tool life and cause low machining surface quality and efficiency in the milling of Ti-6AI-4 V thin-walled workpieces. The influence mechanism between the tool flank wear and stability predication is of significant for its further development in milling Ti-6Al-4 V. However, the relationship between tool flank wear and stability predication needs to be further investigated as the effect of time-varying tool flank wear is ignored in conventional methods. In this work, a system stability prediction model considering time-varying tool flank wear effect in milling of Ti-6AI-4 V thin-walled workpiece is proposed. The tool flank wear region is discretized into differential elements, and then, the friction effect and process damping effect caused by extruding function between tool and workpiece are analyzed, and a time-varying milling force model is established. In this process, the relationship of cutting tool flank wear band width VB and section radius difference NB is determined, and the indentation volume between tool and workpiece is iteratively calculated, which is used to investigate process damping. After, the time-varying milling force coefficients are derived considering different tool flank wear status. Then, in modal space, the evolutionary process of stability lobe diagrams considering tool flank wear effect is determined. Subsequently, to effectively predict system stability, tool flank wear curves and dynamic cutting force coefficients are calibrated by slot milling, and the average errors between cutting force prediction values considering tool flank wear effect and experimental values in feed direction and normal direction are 7.3% and 12.1%, respectively. Finally, a series of machining tests are conducted to verify the effectiveness of tool flank wear on the machining stability in the milling of Ti-6AI-4 V thin-walled workpieces to some extent, and the experimental results show that the system stability prediction accuracy of the proposed method is improved by 23.8% compared with that using conventional method.

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Availability of data and material

The data that support the findings of this study are available from corresponding author upon reasonable request.

Code availability

The codes that support the findings of this study are available from corresponding author upon reasonable request.

References

  1. Zhu LD, Liu CF (2020) Recent progress of chatter prediction, detection and suppression in milling. Mech Syst Signal Process 143:106840. https://doi.org/10.1016/j.ymssp.2020.106840

    Article  Google Scholar 

  2. Zareena AR, Veldhuis SC (2012) Tool wear mechanisms and tool life enhancement in ultra-precision machining of titanium. J Mater Process Technol 212:560–570. https://doi.org/10.1016/j.jmatprotec.2011.10.014

    Article  Google Scholar 

  3. Liang XL, Liu ZQ, Wang B, Hou X (2018) Modeling of plastic deformation induced by thermo-mechanical stresses considering tool flank wear in high-speed machining Ti-6Al-4V. Int J Mech Sci 140:1–12. https://doi.org/10.1016/j.ijmecsci.2018.02.031

    Article  Google Scholar 

  4. Gao Q, Guo GY, Cai M (2021) Wear mechanism and experimental study of a tool used for micro-milling single-crystal nickel-based superalloys. Int J Adv Manuf Technol 113:117–129. https://doi.org/10.1007/s00170-020-06428-x

    Article  Google Scholar 

  5. Khatri A, Jahan MP, Ma JF (2019) Assessment of tool wear and microstructural alteration of the cutting tools in conventional and sustainable slot milling of Ti-6Al-4V alloy. Int J Adv Manuf Technol 105:2799–2814. https://doi.org/10.1007/s00170-019-04520-5

    Article  Google Scholar 

  6. An QL, Chen J, Tao ZR, Ming WW, Chen M (2020) Experimental investigation on tool wear characteristics of PVD and CVD coatings during face milling of Ti-6242S and Ti-555 titanium alloys. Int J Refract Met H 86:105091. https://doi.org/10.1016/j.ijrmhm.2019.105091

    Article  Google Scholar 

  7. Ma JY, Luo DC, Liao XP, Zhang ZK, Huang Y, Lu J (2021) Tool wear mechanism and prediction in milling TC18 titanium alloy using deep learning. Measurement 173:108554. https://doi.org/10.1016/j.measurement.2020.108554

    Article  Google Scholar 

  8. Liu EL, Wang RY, Zhang Y, An WZ (2021) Tool wear analysis of cutting Ti-5553 with uncoated carbide tool under liquid nitrogen cooling condition using tool wear maps. J Manuf Process 68:877–887. https://doi.org/10.1016/j.jmapro.2021.06.016

    Article  Google Scholar 

  9. Zhu KP, Zhang Y (2019) A generic tool wear model and its application to force modeling and wear monitoring in high speed milling. Mech Syst Signal Process 115:147–161. https://doi.org/10.1016/j.ymssp.2018.05.045

    Article  Google Scholar 

  10. Zhang Y, Zhu KP, Duan XY, Li S (2021) Tool wear estimation and life prognostics in milling: model extension and generalization. Mech Syst Signal Process 155:107617. https://doi.org/10.1016/j.ymssp.2021.107617

    Article  Google Scholar 

  11. Feng YX, Hung TP, Lu YT, Lin YF, Hsu FC, Lin CF, Lu YC, Liang SY (2019) Flank tool wear prediction of laser-assisted milling. J Manuf Process 43:292–299. https://doi.org/10.1016/j.jmapro.2019.05.008

    Article  Google Scholar 

  12. Li YG, Liu CQ, Hua JQ, Gao J, Maropoulos P (2019) A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning. CIRP Ann - Manuf Technol 68:487–490. https://doi.org/10.1016/j.cirp.2019.03.010

    Article  Google Scholar 

  13. Hua JQ, Li YG, Mou WP, Liu CQ. An accurate cutting tool wear prediction method under different cutting conditions based on continual learning. P I Mech Eng B-J Eng 2021;236. https://doi.org/10.1177/0954405421993694.

  14. Zhang XW, Yu TB, Zhao J (2020) Surface generation modeling of micro milling process with stochastic tool wear. Precis Eng 61:170–181. https://doi.org/10.1016/j.precisioneng.2019.10.015

    Article  Google Scholar 

  15. Hou YF, Zhang DH, Wu BH, Lou M (2015) Modeling of worn tool and tool flank wear recognition in end milling. IEEE/ASME T Mech 20:1024–1035. https://doi.org/10.1109/TMECH.2014.2363166

    Article  Google Scholar 

  16. Orra K, Choudhury SK (2018) Mechanistic modelling for predicting cutting forces in machining considering effect of tool nose radius on chip formation and tool wear land. Int J Mech Sci 142–143:255–268. https://doi.org/10.1016/j.ijmecsci.2018.05.004

    Article  Google Scholar 

  17. Sun YJ, Sun J, Wang GQ, Du J, Zhang P (2020) A modified analytical cutting force prediction model under the tool crater wear effect in end milling Ti6Al4V with solid carbide tool. Int J Adv Manuf Technol 108:3475–3490. https://doi.org/10.1007/s00170-020-05579-1

    Article  Google Scholar 

  18. Chiou RY, Liang SY (1998) Chatter stability of a slender cutting tool in turning with tool wear effect. Int J Mach Tools Manuf 38:315–327. https://doi.org/10.1016/S0890-6955(97)00079-5

    Article  Google Scholar 

  19. Tyler CT, Troutman J, Schmitz T (2015) Radial depth of cut stability lobe diagrams with process damping effects. Precis Eng 40:318–324. https://doi.org/10.1016/j.precisioneng.2014.11.004

    Article  Google Scholar 

  20. Moradi H, Movahhedy MR, Gholamreza Vossoughi. Bifurcation analysis of milling process with tool wear and process damping: regenerative chatter with primary resonance. Nonlinear Dyn 2012;70:481–509. https://doi.org/10.1007/s11071-012-0470-7.

  21. Afazov SM, Zdebski D, Ratchev SM, Segal J, Liu S (2013) Effects of micro-milling conditions on the cutting forces and process stability. J Mater Process Technol 213:671–684. https://doi.org/10.1016/j.jmatprotec.2012.12.001

    Article  Google Scholar 

  22. Feng J, Wan M, Gao TQ, Zhang WH (2018) Mechanism of process damping in milling of thin-walled workpiece. Int J Mach Tools Manuf 134:1–19. https://doi.org/10.1016/j.ijmachtools.2018.06.001

    Article  Google Scholar 

  23. Feng J, Wan M, Dong ZY, Zhang WH (2019) A unified process damping model considering the varying stiffness of the milling system. Int J Mach Tools Manuf 147:103470. https://doi.org/10.1016/j.ijmachtools.2019.103470

    Article  Google Scholar 

  24. Tang XW, Peng FY, Yan R, Zhu ZR, Li ZP, Xin SH (2021) Nonlinear process damping identification using finite amplitude stability and the influence analysis on five-axis milling stability. Int J Mech Sci 190:106008. https://doi.org/10.1016/j.ijmecsci.2020.106008

    Article  Google Scholar 

  25. Munoa J, Beudaert X, Dombovari Z, Altintas Y, Budak E, Brecher C, Stepan G (2016) Chatter suppression techniques in metal cutting. CIRP Ann - Manuf Technol 65:785–808. https://doi.org/10.1016/j.cirp.2016.06.004

    Article  Google Scholar 

  26. Denkena B, Krödel A, Relard A (2021) Using tool wear to increase process stability when milling Al7075 and AISI 4140+QT. Prod Eng 15:843–853. https://doi.org/10.1007/s11740-021-01059-x

    Article  Google Scholar 

  27. Teitenberg TM, Bayoumi AE, Yuscesan G (1992) Tool wear modeling through an analytic mechanistic model of milling processes. Wear 154:287–304. https://doi.org/10.1016/0043-1648(92)90160-A

    Article  Google Scholar 

  28. Smithey DW, Kapoor SG, Devor RE (2001) A new mechanistic model for predicting worn tool cutting forces. Mach Sci Technol 5:23–42. https://doi.org/10.1081/MST-100103176

    Article  Google Scholar 

  29. Wu DW (1989) A new approach of formulating the transfer function for dynamic cutting processes. J Eng Ind 111:37–47. https://doi.org/10.1115/1.3188730

    Article  Google Scholar 

  30. Ahmadi K, Ismail F (2011) Analytical stability lobes including nonlinear process damping effect on machining chatter. Int J Mach Tools Manuf 51:293–308. https://doi.org/10.1016/j.ijmachtools.2010.12.008

    Article  Google Scholar 

  31. Ma JJ, Li YF, Zhang DH, Zhao B, Wang G, Pang XY (2022) A novel updated full-discretization method for prediction of milling stability. Micromachines 13:160. https://doi.org/10.3390/mi13020160

    Article  Google Scholar 

  32. Li S, Zhu KP (2021) In-situ tool wear area evaluation in micro milling with considering the influence of cutting force. Mech Syst Signal Process 161. https://doi.org/10.1016/j.ymssp.2021.107971

  33. Altan E, Uysal A, Caliskan O (2018) Investigation into the effectiveness of cutting parameters on wear regions of the flank wear curve and associated cutting tool life improvement. Int J Mater Prod Tec 57:54–70. https://doi.org/10.1504/IJMPT.2018.092931

    Article  Google Scholar 

  34. Altintas Y (2000) Manufacturing automation: metal cutting mechanics, machine tool vibration, and CNC design, 2nd edn. Cambridge University Press, Cambridge

    Google Scholar 

Download references

Funding

The authors are grateful to the National Natural Science Foundation of China (No. 52005166), the Postdoctoral Research Foundation of China (No. 2019M652534), the Henan Postdoctoral Foundation (No. 19030071), the Foundation of Henan Educational Committee (No. 20A460016), the Young Backbone Teachers Foundation Scheme of Henan Polytechnic University (No. 2019XQG-01), and the National Science Fund for Distinguished Young Scholars of Henan Polytechnic University (No. J2022-5).

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Authors and Affiliations

  1. School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo, 454000, China

    Junjin Ma, Yunfei Li, Bo Zhao & Xinhong Yan

  2. Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi’an, 710072, China

    Dinghua Zhang

  3. College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, China

    Xiaoyan Pang

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  1. Junjin Ma
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Contributions

Junjin Ma, conceptualization, methodology, formal analysis, investigation, and writing — original draft; Yunfei Li, data curation, experimental verification, and methodology; Dinghua Zhang, writing — review and editing, and supervision; Bo Zhao, validation, writing — review and editing, and supervision; Xinhong Yan, data processing and experimental verification; Xiaoyan Pang, data processing, visualization, and investigation.

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Correspondence to Junjin Ma.

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Ma, J., Li, Y., Zhang, D. et al. Investigation of tool flank wear effect on system stability prediction in the milling of Ti-6AI-4 V thin-walled workpiece. Int J Adv Manuf Technol 122, 3937–3956 (2022). https://doi.org/10.1007/s00170-022-10136-z

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