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Research on automatic monitoring method of face milling cutter wear based on dynamic image sequence

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Abstract

Tool wear is an important factor affecting the quality of finished products, productivity, and the normal operation of machine tools, so tool condition monitoring (TCM) has become a research hotpot in the field of intelligent manufacturing. Compared with traditional monitoring methods, vision-based tool condition monitoring methods are more accurate and intuitive. However, the existing visual monitoring method requires manual adjustment of the tool position, and the degree of automation needs to be improved. Therefore, this paper proposes automatic face milling cutter condition monitoring method based on dynamic image sequence. We first acquire the dynamic image sequence of face milling cutter with the spindle rotating, then forward the dynamic image sequence to the image processing module to extract target area. And the images after image processing are propagated to the image selection module to obtain the image to be measured. Finally, forward the selected image to wear value measurement module to obtain the wear value. The presented automatic face milling cutter condition monitoring method is verified on a five-axis milling center. Compared with the direct measurement results of industrial digital microscope, the measurement error of the proposed method is within 4%, which is a reliable and effective online monitoring method for milling cutter wear.

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References

  1. Kurada S, Bradley C (1997) A review of machine vision sensors for tool condition monitoring. Compute Ind 34:55–72

    Google Scholar 

  2. Rehorn AG, Jiang J, Orban PE (2005) State-of-the-art methods and results in tool condition monitoring: a review. Int J Adv Manuf Technol 26(7–8):693–710. https://doi.org/10.1007/s00170-004-2038-2

    Article  Google Scholar 

  3. Guo L, Gao H, Huang H, He X, Li S (2016) Multifeatures fusion and nonlinear dimension reduction for intelligent bearing condition monitoring. Shock and Vibration 2016:1–10. https://doi.org/10.1155/2016/46325

    Article  Google Scholar 

  4. Dutta S, Pal SK, Mukhopadhyay S, Sen R (2013) Application of digital image processing in tool condition monitoring: a review. CIRP J Manuf Sci Technol 6(3):212–232. https://doi.org/10.1016/j.cirpj.2013.02.005

    Article  Google Scholar 

  5. Siddhpura A, Paurobally R (2013) A review of flank wear prediction methods for tool condition monitoring in a turning process. Int J Adv Manuf Technol 65(1-4):371–393. https://doi.org/10.1007/s00170-012-4177-1

    Article  Google Scholar 

  6. Ahmad MI, Yusof Y, Daud ME, Latiff K, Kadir AZA, Saif Y (2020) Machine monitoring system: a decade in review. Int J Adv Manuf Technol 108:3645–3659. https://doi.org/10.1007/s00170-020-05620-3

    Article  Google Scholar 

  7. Zhou L, Deng B, Peng F, Yan R, Yang M, Sun H (2020) Analytical modelling and experimental validation of micro-ball-end milling forces with progressive tool flank wear. Int J Adv Manuf Technol 108:3335–3349. https://doi.org/10.1007/s00170-020-05574-6

    Article  Google Scholar 

  8. Li N, Chen Y, Kong D, Tan S (2017) Force-based tool condition monitoring for turning process using v-support vector regression. Int J Adv Manuf Technol 91:351–361. https://doi.org/10.1007/s00170-016-9735-5

    Article  Google Scholar 

  9. García Plaza E, Núñez López PJ (2018) Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations. Mech Syst Signal Process 98:902–919. https://doi.org/10.1016/j.ymsp.2017.05.028

    Article  Google Scholar 

  10. Nasir V, Cool J (2020) Intelligent wood machining monitoring using vibration signals combined with self-organizing maps for automatic feature selection. Int J Adv Manuf Technol 108:1811–1825. https://doi.org/10.1007/s00170-020-05505-5

    Article  Google Scholar 

  11. Bhuiyan MSH, Choudhury IA, Dahari M, Nukman Y, Dawal SZ (2016) Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring. Measurement 92:208–217. https://doi.org/10.1016/j.measurement.2016.06.006

    Article  Google Scholar 

  12. Liu M, Tseng Y, Tran M (2019) Tool wear monitoring and prediction based on sound signal. Int J Adv Manuf Technol 103:3361–3373. https://doi.org/10.1007/s00170-019-03686-2

    Article  Google Scholar 

  13. Neef B, Bartels J, Thiede J. Tool wear and surface quality monitoring using high frequency CNC machine tool current signature. 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), Porto, 2018, pp. 1045–1050. doi:https://doi.org/10.1109/INDIN.2018.8472037

  14. Brezak D, Majetic D, Udiljak T, Kasac J (2010) Tool wear estimation using an analytic fuzzy classifier and support vector machines. Journal of Intelligent Manufacturing 23(3):797–809. https://doi.org/10.1007/s10845-010-0436-x

    Article  Google Scholar 

  15. Bhuiyan MSH, Choudhury IA, Dahari M (2014) Monitoring the tool wear, surface roughness and chip formation occurrences using multiple sensors in turning. J Manuf Syst 33(4):476–487. https://doi.org/10.1016/j.jmsy.2014.04.005

    Article  Google Scholar 

  16. Huang Z, Zhu J, Lei J, Li X, Tian F (2019) Tool wear predicting based on multisensory raw signals fusion by reshaped time series convolutional neural network in manufacturing[J]. IEEE Access 7:178640–178651. https://doi.org/10.1109/ACCESS.2019.2958330

    Article  Google Scholar 

  17. Sortino M (2003) Application of statistical filtering for optical detection of tool wear. Int J Mach Tool Manu 43(5):493–497. https://doi.org/10.1016/S0890-6955(02)00266-3

    Article  Google Scholar 

  18. Kwon Y, Fischer GW (2003) A novel approach to quantifying tool wear and tool life measurements for optimal tool management. Int J Mach Tool Manu 43(4):359–368. https://doi.org/10.1016/S0890-6955(02)00271-7

    Article  Google Scholar 

  19. Su JC, Huang CK, Tarng YS (2006) An automated flank wear measurement of microdrills using machine vision. J Mater Process Technol 180(1–3):328–335. https://doi.org/10.1016/j.jmtprotec.2006.07.001

    Article  Google Scholar 

  20. Ong P, Lee WK, Lau RJH (2019) Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision. Int J Adv Manuf Technol 104(1–4):1369–1379. https://doi.org/10.1007/s00170-019-04020-6

    Article  Google Scholar 

  21. Jurkovic J, Korosec M, Kopac J (2005) New approach in tool wear measuring technique using CCD vision system. Int J Mach Tool Manu 45(9):1023–1030. https://doi.org/10.1016/j.ijmachtools.2004.11.030

    Article  Google Scholar 

  22. Pfeifer T, Wiegers L (2000) Reliable tool wear monitoring by optimized image and illumination control in machine vision. Measurement 28(3):209–218

    Google Scholar 

  23. Wang H, Wang H (2018) A detection system of tool parameter using machine vision // 2018 37th Chinese Control Conference (CCC). IEEE

  24. Wang WH, Hong GS, Wong YS (2006) Flank wear measurement by a threshold independent method with sub-pixel accuracy. Int J Mach Tools Manuf 46(2):199–207. https://doi.org/10.1016/j.ijmachtools.2005.04.006

    Article  Google Scholar 

  25. Wang W, Wong YS, Hong GS (2005) Flank wear measurement by successive image analysis. Comput Ind 56(8–9):816–830. https://doi.org/10.1016/j.compind.2005.05.009

    Article  Google Scholar 

  26. Zhang Y, Zhang Y, Tang H, Wang L (2009) Images acquisition of a high-speed boring cutter for tool condition monitoring purposes. Int J Adv Manuf Technol 48(5–8):455–460. https://doi.org/10.1007/s00170-009-2311-5

    Article  Google Scholar 

  27. Zhang J, Zhang C, Guo S, Zhou L (2012) Research on tool wear detection based on machine vision in end milling process. Prod Eng 6(4–5):431–437. https://doi.org/10.1007/s11740-012-0

    Article  Google Scholar 

  28. Sun WH, Yeh SS (2018) Using the machine vision method to develop an on-machine insert condition monitoring system for computer numerical control turning machine tools. Materials 11(10). https://doi.org/10.3390/ma11101977

  29. Yu X, Lin X, Dai Y, Zhu K (2017) Image edge detection based tool condition monitoring with morphological component analysis. ISA Trans 69:315–322. https://doi.org/10.1016/j.isatra.2017.03.0

    Article  Google Scholar 

  30. Zhu K, Yu X (2017) The monitoring of micro milling tool wear conditions by wear area estimation. Mech Syst Signal Process 93:80–91. https://doi.org/10.1016/j.ymssp.2017.02.00

    Article  Google Scholar 

  31. Lachance S, Bauer R, Warkentin A (2004) Application of region growing method to evaluate the surface condition of grinding wheels. Int J Mach Tools Manuf 44(7-8):823–829. https://doi.org/10.1016/j.ijmachtools.2004.01.006

    Article  Google Scholar 

  32. Hou Q, Sun J, Lv Z, Huang P, Song G, Sun C (2019) An online tool wear detection system in dry milling based on machine vision. Int J Adv Manuf Technol 105(1–4):1801–1810. https://doi.org/10.1007/s00170-019-04367-w

    Article  Google Scholar 

  33. Barreiro J, Castejón M, Alegre E, Hernández LK (2008) Use of descriptors based on moments from digital images for tool wear monitoring. Int J Mach Tools Manuf 48(9):1005–1013. https://doi.org/10.1016/j.ijmachtools.2008.01.005

    Article  Google Scholar 

  34. García-Ordás MT, Alegre E, González-Castro V, García-Ordás D (2014) aZIBO: a new descriptor based in shape moments and rotational invariant feature // international conference in pattern recognition 2014 (ICPR 2014). IEEE Computer Society. https://doi.org/10.1109/ICPR.2014.415

  35. García-Ordás MT, Alegre E, González-Castro V, Alaiz-Rodríguez R (2016) A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques. Int J Adv Manuf Technol 90(5–8):1947–1961. https://doi.org/10.1007/s00170-016-9541-0

    Article  Google Scholar 

  36. García-Ordás MT, Alegre-Gutiérrez E, González-Castro V, Alaiz-Rodríguez R (2018) Combining shape and contour features to improve tool wear monitoring in milling processes. Int J Prod Res 56(11):3901–3913. https://doi.org/10.1080/00207543.2018.1435919

    Article  Google Scholar 

  37. García-Ordás MT, Alegre-Gutiérrez E, Alaiz-Rodríguez R, González-Castro V (2018) Tool wear monitoring using an online, automatic and low cost system based on local texture. Mech Syst Signal Process 112:98–112. https://doi.org/10.1016/j.ymssp.2018.04.035

    Article  Google Scholar 

  38. Lins RG, de Araujo PRM, Corazzim M (2020) In-process machine vision monitoring of tool wear for cyber-physical production systems. Robotics and Computer-Integrated Manufacturing 61. doi:https://doi.org/10.1016/j.rcim.2019.101859

  39. Lins RG, Guerreiro B, Marques de Araujo PR, Schmitt R (2019) In-process tool wear measurement system based on image analysis for CNC drilling machines. IEEE Trans Instrum Meas 69(8):5579–5588. https://doi.org/10.1109/TIM.2019.2961572

    Article  Google Scholar 

  40. Jaccard P (1912) The distribution of the flora of the alpine zone[J]. New Phytol 11(2):37–50

    Google Scholar 

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 51775452, Grant 51905452, in part by Fundamental Research Funds for Central Universities under Grant 2682017ZDPY09, Grant 2682019CX35, and in part by Planning Project of Science & Technology Department of Sichuan Province under Grant 2019YFG0353.

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Authors and Affiliations

  1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Aoping Qin, Liang Guo, Zhichao You, Hongli Gao, Xiangdong Wu & Shoubing Xiang

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  1. Aoping Qin
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  2. Liang Guo
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  3. Zhichao You
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  4. Hongli Gao
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Correspondence to Hongli Gao.

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Qin, A., Guo, L., You, Z. et al. Research on automatic monitoring method of face milling cutter wear based on dynamic image sequence. Int J Adv Manuf Technol 110, 3365–3376 (2020). https://doi.org/10.1007/s00170-020-05955-x

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