Skip to main content
SpringerLink
Log in

Micro-milling tool wear monitoring under variable cutting parameters and runout using fast cutting force coefficient identification method

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Extracting discriminative tool wear features is of great importance for tool wear monitoring in micro-milling. However, due to the dependency on tool runout and cutting parameters, the traditional tool wear features are incompetent to monitor the tool wear condition in micro-milling with significant tool runout and varied cutting parameter interactions. In this study, micro-milling cutting force is represented by a parametric model including variable cutting parameters, tool runout, and tool wear. The cutting force coefficient in the model, which is not only discriminative to the tool wear condition but also independent to the tool runout and cutting parameters, is extracted as the micro-milling tool wear feature. To reduce the computation cost, a fast neural network–based method is proposed to identify the tool runout and the cutting force coefficient from the cutting force signal. Experimental results show that the proposed cutting force coefficient–based approach is efficient to monitor the micro-milling tool wear under varied cutting parameters and tool runout.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Câmara MA, Campos Rubio JC, Abrão AM, Davim JP (2012) State of the art on micromilling of materials, a review. J Mater Sci Technol 28(8):673–685

    Google Scholar 

  2. Chae J, Park SS, Freiheit T (2006) Investigation of micro-cutting operations. Int J Mach Tools Manuf 46(3):313–332

    Google Scholar 

  3. Alhadeff LL, Marshall MB, Curtis DT, Slatter T (2019) Protocol for tool wear measurement in micro-milling. Wear 420:54–67

    Google Scholar 

  4. Malekian M, Park SS, Jun MBG (2009) Tool wear monitoring of micro-milling operations. J Mater Process Technol 209(10):4903–4914

    Google Scholar 

  5. Zhu KP, Mei T, Ye DS (2015) Online condition monitoring in micromilling: a force waveform shape analysis approach. IEEE Trans Ind Electron 62(6):3806–3813

    Google Scholar 

  6. Zhou YQ, Wei X (2018) Review of tool condition monitoring methods in milling processes. Int J Adv Manuf Technol 96:2509–2523

    Google Scholar 

  7. Mohanraj T, Shankar S, Rajasekar R, Sakthivel NR, Pramanik A (2020) Tool condition monitoring techniques in milling process-a review. J Mater Res Technol-JMRT 9(1):1032–1042

    Google Scholar 

  8. Jurkovic J, Korosec M, Kopac J (2005) New approach in tool wear measuring technique using CCD vision system. Int J Mach Tools Manuf 45(9):1023–1030

    Google Scholar 

  9. Zhu KP, Yu XL (2017) The monitoring of micro milling tool wear conditions by wear area estimation. Mech Syst Signal Process 93:80–91

    Google Scholar 

  10. Tansel IN, Arkan TT, Bao WY, Mahendrakar N, Shisler B, Smith D, McCool M (2000) Tool wear estimation in micro-machining.: part I: tool usage–cutting force relationship. Int J Mach Tools Manuf 40(4):599–608

    Google Scholar 

  11. Rmili W, Ouahabi A, Serra R, Leroy R (2016) An automatic system based on vibratory analysis for cutting tool wear monitoring. Measurement 77:117–123

    Google Scholar 

  12. Zhou C, Yang B, Guo K, Liu J, Sun J, Song G, Zhu S, Sun C, Jiang Z (2020) Vibration singularity analysis for milling tool condition monitoring. Int J Mech Sci 166:105254

    Google Scholar 

  13. Mathew MT, Pai PS, Rocha LA (2008) An effective sensor for tool wear monitoring in face milling: acoustic emission. Sadhana-Acad Proc Eng Sci 33(3):227–233

    Google Scholar 

  14. Liu TS, Zhu KP, Zeng LC (2018) Diagnosis and prognosis of degradation process via hidden semi-Markov model. IEEE ASME Trans Mechatron 23(3):1456–1466

    Google Scholar 

  15. Ghosh N, Ravi YB, Patra A, Mukhopadhyay S, Paul S, Mohanty AR, Chattopadhyay AB (2007) Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mech Syst Signal Process 21(1):466–479

    Google Scholar 

  16. Hsieh WH, Lu MC, Chiou SJ (2012) Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling. Int J Adv Manuf Technol 61:53–61

    Google Scholar 

  17. Chen Z, Zhang H (2016) Modelling and prediction of tool wear using LS-SVM in milling operation. Int J Comput Integr Manuf 29(1):76–91

    Google Scholar 

  18. Yang Y, Guo Y, Huang Z, Chen N, Li L, Jiang YF, He N (2019) Research on the milling tool wear and life prediction by establishing an integrated predictive model. Measurement 145:178–189

    Google Scholar 

  19. Liao ZR, Gao D, Lu Y, Lv ZK (2016) Multi-scale hybrid HMM for tool wear condition monitoring. Int J Adv Manuf Technol 84:2437–2448

    Google Scholar 

  20. Geramifard O, Xu JX, Zhou JH, Li X (2012) A physically segmented hidden Markov model approach for continuous tool condition monitoring: diagnostics and prognostics. IEEE Trans Ind Inf 8(4):964–973

    Google Scholar 

  21. Kong D, Chen Y, Li N (2017) Hidden semi-Markov model-based method for tool wear estimation in milling process. Int J Adv Manuf Technol 92:3647–3657

  22. An H, Wang GF, Dong Y, Yang K, Sang LL (2019) Tool life prediction based on Gauss importance resampling particle filter. Int J Adv Manuf Technol 103:4627–4634

    Google Scholar 

  23. Niaki FA, Michel M, Mears L (2016) State of health monitoring in machining: extended Kalman filter for tool wear assessment in turning of IN718 hard-to-machine alloy. J Manuf Process 24(2):361–369

    Google Scholar 

  24. Cai W, Zhang W, Hu X, Liu YC (2020) A hybrid information model based on long short-term memory network for tool condition monitoring. J Intell Manuf 31(6):1497–1510

  25. Wang P, Liu Z, Gao RX, Guo Y (2019) Heterogeneous data-driven hybrid machine learning for tool condition prognosis. CIRP Ann Manuf Technol 68:455–458

    Google Scholar 

  26. Chu CH, Lee CT, Tien KW (2011) Efficient tool path planning for 5-axis flank milling of ruled surfaces using ant colony system algorithms. Int J Prod Res 49(6):1557–1574

    Google Scholar 

  27. Saikumar S, Shunmugam MS (2012) Development of a feed rate adaption control system for high speed rough and finish end-milling of hardened EN24 steel. Int J Adv Manuf Technol 59:869–884

    Google Scholar 

  28. Zhang X, Pan X, Wang G (2019) A new method for determining the instantaneous uncut chip thickness in micro-milling. Int J Adv Manuf Technol 102(9):3791–3800

    Google Scholar 

  29. Bao WY, Tansel IN (2000) Modeling micro-end-milling operations, Part II: Tool run-out. Int J Mach Tools Manuf 40(15):2175–2192

  30. Fu Y, Zhang Y, Qiao H (2015) Analysis of feature extracting ability for cutting state monitoring using deep belief networks. Procedia Cirp 31(Suppl. C):29–34

    Google Scholar 

  31. Nouri M, Fussell BK, Ziniti BL, Linder E (2015) Real-time tool wear monitoring in milling using a cutting condition independent method. Int J Mach Tools Manuf 89:1–13

    Google Scholar 

  32. Hou YF, Zhang DH, Wu BH, Luo M (2015) Milling force modeling of worn tool and tool flank wear recognition in end milling. IEEE ASME Trans Mechatron 20(3):1024–1035

    Google Scholar 

  33. Chen N, Li L, Wu J, Qian J, He N, Reynaerts D (2019) Research on the ploughing force in micro milling of soft-brittle crystals. Int J Mech Sci 155:315–322

    Google Scholar 

  34. Li KX, Zhu KP, Mei T (2016) A generic instantaneous undeformed chip thickness model for the cutting force modeling in micromilling. Int J Mach Tools Manuf 105:23–31

  35. Yuan YJ, Jing XB, Ehmann KF, Cao J, Li HZ, Zhang DW (2018) Modeling of cutting forces in micro end-milling. J Manuf Process 31:844–858

    Google Scholar 

  36. Wojciechowski S, Mrozek K (2017) Mechanical and technological aspects of micro ball end milling with various tool inclinations. Int J Mech Sci 134:424–435

    Google Scholar 

  37. Sahoo P, Pratap T, Patra K (2019) A hybrid modelling approach towards prediction of cutting forces in micro end milling of Ti-6Al-4V titanium alloy. Int J Mech Sci 150:495–509

  38. Lu XH, Wang FR, Jia ZY, Si LK, Zhang C, Liang SY (2017) A modified analytical cutting force prediction model under the tool flank wear effect in micro-milling nickel-based superalloy. Int J Adv Manuf 91:3709–3716

    Google Scholar 

  39. Zhou L, Deng B, Peng FY (2020) Semi-analytic modelling of cutting forces in micro ball-end milling of NAK80 steel with wear-varying cutting edge and associated nonlinear process characteristics. Int J Mech Sci 169:105343

    Google Scholar 

  40. Li GC, Li S, Zhu KP (2020) Micro-milling force modeling with tool wear and runout effect by spatial analytic geometry. Int J Adv Manuf 107(1):631–643

  41. Jing XB, Tian YL, Yuan YJ, Wang FJ (2017) A runout measuring method using modeling and simulation cutting force in micro end-milling. Int J Adv Manuf 91:4191–4201

    Google Scholar 

  42. Lee C, Zhao R, Jeon S (2017) A simple optical system for miniature spindle runout monitoring. Measurement 102:42–46

    Google Scholar 

  43. Singh KK, Kartik V, Singh R (2018) Stability modeling with dynamic run-out in high speed micromilling of Ti6Al4V. Int J Mech Sci 150:677–690

    Google Scholar 

  44. Ko JH, Cho DW (2005) 3D ball-end milling force model using instantaneous cutting force coefficients. J Manuf Sci Eng Trans ASME 127(1):1–12

    Google Scholar 

  45. Yun WS, Cho DW (2000) An improved method for the determination of 3D cutting force coefficients and runout parameters in end milling. Int J Adv Manuf Technol 16(12):851–858

    Google Scholar 

  46. Grossi N, Sallese L, Scippa A (2015) Speed-varying cutting force coefficient identification in milling. Precis Eng 2:321–334

    Google Scholar 

  47. Wan M, Zhang WH, Qin GH, Tan G (2007) Efficient calibration of instantaneous cutting force coefficients and runout parameters for general end mills. Int J Mach Tools Manuf 47(11):1767–1776

    Google Scholar 

  48. Zhou YD, Tian YL, Jing XB, Ehmann KF (2017) A novel instantaneous uncut chip thickness model for mechanistic cutting force model in micro-end-milling. Int J Adv Manuf 93:2305–2319

    Google Scholar 

  49. Wojciechowski S, Matuszak M, Powałka B, Madajewski M, Maruda RW, Krolczyk GM (2019) Prediction of cutting forces during micro end milling considering chip thickness accumulation. Int J Mach Tools Manuf 147:103466

    Google Scholar 

  50. Zhu KP, Zhang Y (2017) Modeling of the instantaneous milling force per tooth with tool run-out effect in high speed ball-end milling. Int J Mach Tools Manuf 118:37–48

  51. Zhang X, Pan X, Wang G, Zhou D (2018) Tool runout and single-edge cutting in micro-milling. Int J Adv Manuf Technol 96(1):821–832

    Google Scholar 

  52. ASTM A600-92a (2016) Standard Specification for Tool Steel High Speed. ASTM International, West Conshohocken www.astm.org

  53. Girardin F, Rémond D, Rigal JF (2010) Tool wear detection in milling—an original approach with a non-dedicated sensor. Mech Syst Signal Process 24(6):1907–1920

    Google Scholar 

  54. Lai XM, Li HT, Li CF, Lin ZQ, Ni J (2008) Modelling and analysis of micro scale milling considering size effect, micro cutter edge radius and minimum chip thickness. Int J Mach Tools Manuf 48:1–14

    Google Scholar 

  55. Mian, AJ (2011) Size Effect in Micromachining. Ph.D. Thesis, The University of Manchester, Faculty of Engineering and Physical Sciences, School of Mechanical, Aerospace and Civil Engineering, Manchester, UK

  56. 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

  57. Shi KN, Zhang DH, Liu N, Wang SB, Ren JX, Wang SL (2018) A novel energy consumption model for milling process considering tool wear progression. J Clean Prod 184:152–159

    Google Scholar 

Download references

Acknowledgments

The first author would like to thank the Lab of Precision Manufacturing, Institute of Advanced Manufacturing Technology, Chinese Academy of Sciences, for providing the experimental data.

Funding

This project is supported by The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No.19KJB460007), National Natural Science Foundation of China (Grant No. 51805341), and Natural Science Foundation of Jiangsu Province (Grant No. BK20180843).

Author information

Authors and Affiliations

  1. School of Mechanical and Electric Engineering, Soochow University, Suzhou, 215021, Jiangsu, China

    Tongshun Liu & Gang Wang

  2. Institute of Advanced Manufacturing Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Changzhou, 213164, Jiangsu, China

    Kunpeng Zhu

Authors
  1. Tongshun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kunpeng Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tongshun Liu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, T., Zhu, K. & Wang, G. Micro-milling tool wear monitoring under variable cutting parameters and runout using fast cutting force coefficient identification method. Int J Adv Manuf Technol 111, 3175–3188 (2020). https://doi.org/10.1007/s00170-020-06272-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-06272-z

Keywords

Search

Navigation