Tool wear predictability estimation in milling based on multi-sensorial data

  • P. Stavropoulos
  • A. Papacharalampopoulos
  • E. Vasiliadis
  • G. Chryssolouris
Open Access
ORIGINAL ARTICLE

Abstract

The safe and reliable operations in industrial manufacturing processes play a crucial role in the economic productivity. Machining process disturbances such as collision, overload, breakdown, and tool wear tend to cause production system failures. The current study aims at investigating the limitations of tool wear prediction on the milling of CGI 450 plates, through the simultaneous detection of acceleration and spindle drive current sensor signals. Tool wear prediction has been accomplished, by utilizing the experimental results that derived from third degree regression models and pattern recognition systems. These results indicate that predictability is affected by the mean signal energy, acquired from the vibration acceleration signals.

Keywords

Tool wear Monitoring Predictability Fused sensorial signals Pattern recognition 

References

  1. 1.
    Chryssolouris G (2006) Manufacturing systems—theory and practice, 2nd edn. Springer, New YorkGoogle Scholar
  2. 2.
    Ji C, Liu Z, Ai X (2014) Effect of cutter geometric configuration on aerodynamic noise generation in face milling cutters. Appl Acoust 75:43–51CrossRefGoogle Scholar
  3. 3.
    Larreina J., Gontarz A., Giannoulis C., Nguyen V.K., Stavropoulos P., Sinceri B. (2013) Smart Manufacturing Execution System (SMES): The possibilities of evaluating the sustainability of a production process, (GCSM) 11th Global Conference on Sustainable Manufacturing, 23-25 September, Berlin, Germany, pp.517–522Google Scholar
  4. 4.
    Wright P.K., Trent E.M. (1974) Metallurgical appraisal of wear mechanisms and processes on high-speed-steel cutting tools, Metals Technology, January 13–23Google Scholar
  5. 5.
    Tarasov SY, Rubtsov VE, Kolubaeva EA (2014) A proposed diffusion-controlled wear mechanism of alloy steel friction stir welding (FSW) tools used on an aluminum alloy. Wear 318(1–2):130–134CrossRefGoogle Scholar
  6. 6.
    Jantunen E (2002) A summary of methods applied to tool condition monitoring in drilling. Int J Mach Tools Manufac 42:997–1010CrossRefGoogle Scholar
  7. 7.
    Salonitis K, Kolios A (2014) Reliability assessment of cutting tool life based on surrogate approximation methods. Int J Adv Manuf Technol 71(5–8):1197–1208CrossRefGoogle Scholar
  8. 8.
    Li H, He G, Qin X, Wang G, Lu C, Gui L (2014) Tool wear and hole quality investigation in dry helical milling of Ti-6Al-4V alloy. Int J Adv Manuf Technol 71:1511–152CrossRefGoogle Scholar
  9. 9.
    Lim G (1995) Tool-wear monitoring in machine turning. J Mat Process Technol 51:25–36CrossRefGoogle Scholar
  10. 10.
    Smith GT (1989) Advanced machining, IFS Publications. Springer, UKGoogle Scholar
  11. 11.
    Wang W. H., Hong G. S., Wong Y. S. and Zhu K. P. (2007) Sensor fusion for online tool condition monitoring in milling, Vol. 45, No. 21, 1 November, 5095–5116Google Scholar
  12. 12.
    Stavropoulos P, Stournaras A, Chryssolouris G (2009) On the design of a monitoring system for desktop micro-milling machines. Int J Nanomanufac 3(1/2):29–39CrossRefGoogle Scholar
  13. 13.
    Kavaratzis Y, Maiden JD (1989) System for real time process monitoring and adaptive control during CNC deep hole drilling, in: proceedings of Comadem ‘89. Kogan Page, London, pp 148–152Google Scholar
  14. 14.
    Jantunen E. (2002) A solution for tool wear diagnosis, in: proceedings, International Journal of Machine Tools & Manufacture 42, 997–1010, 1999, pp. 95–104Google Scholar
  15. 15.
    Li X (1999) On-line detection of the breakage of small diameter drills using current signature wavelet transform. Int J Mach Tools Manufac 39(1):157–164CrossRefGoogle Scholar
  16. 16.
    Lee DE, Hwang I, Valente CMO, Oliveira JFG, Dornfeld DA (2006) Precision manufacturing process monitoring with acoustic emission. Int J Mach Tools Manufac 46:176–188CrossRefGoogle Scholar
  17. 17.
    Jemielniak K, Arrazola PJ (2008) Application of AE and cutting force signals in tool condition monitoring in micro-milling. CIRP J Manuf Sci Technol 1:97–102CrossRefGoogle Scholar
  18. 18.
    Cao H, Chena X, Zia Y (2008) End milling tool breakage detection using lifting scheme and Mahalanobis distance. Int J Mach Tools Manufac 48:141–151CrossRefGoogle Scholar
  19. 19.
    Chryssolouris G., Guillot M., Domroese M. (1987) Tool wear estimation for intelligent machining, symposium on intelligent control, ASME Winter Annual Meeting, Boston, Massachusetts, pp. 35–43Google Scholar
  20. 20.
    Principe C, Yoon T (1991) A new algorithm for the detection of tool breakage in milling. Int J Mach Tools Manufac 31:443–454CrossRefGoogle Scholar
  21. 21.
    Stavropoulos P., Salonitis K., Stournaras A., Pandremenos J., Paralikas J., Chryssolouris G. (2007) Experimental investigation of micro-milling process quality, 40th CIRP International Seminar on Manufacturing Systems, LiverpoolGoogle Scholar
  22. 22.
    Ertekin YM, Kwon Y, Tseng B (2003) Identification of common sensory features for the control of CNC milling operations under varying cutting conditions. Int J Mach Tools Manufac 43:897–904CrossRefGoogle Scholar
  23. 23.
    Stavropoulos P., Salonitis K., Stournaras A., Pandremenos J., Paralikas J., Chryssolouris G. (2007), Advances and challenges for tool condition monitoring in micro-milling, IFAC Workshop on Manufacturing Modelling, Management and Control, pp. 157–162Google Scholar
  24. 24.
    Kluft, W. (1983) Werkzeuguberwachungssysteme furs die Drehbearbeitung, doctoral thesis, RWTH AachenGoogle Scholar
  25. 25.
    Prickett PW, Johns C (1999) An overview of approaches to end milling tool monitoring. Int J Mach Tools Manuf 39:105–122CrossRefGoogle Scholar
  26. 26.
    Ritou M, Garniera S, Fureta B, Hascoet JY (2014) Angular approach combined to mechanical model for tool breakage detection by eddy current sensors. Mech Syst Signal Process 44(1–2):211–220CrossRefGoogle Scholar
  27. 27.
    Chryssolouris G., Domroese M. (1988) Sensor integration for tool wear estimation in machining, symposium on sensors and controls for manufacturing, ASME Winter Annual Meeting, Chicago, Illinois, USA, pp. 115–123Google Scholar
  28. 28.
    Chryssolouris G (1982) Effects of machine-tool-workpiece stiffness on the wear behavior of superhard cutting materials. CIRP Ann 31(1):65–69CrossRefGoogle Scholar
  29. 29.
    Segreto T., Simeone A., Teti R. (2013) Multiple sensor monitoring in nickel alloy turning for tool wear assessment via sensor fusion, Procedia CIRP 12, 85–90, 8th CIRP Conference on Intelligent Computation in Manufacturing EngineeringGoogle Scholar
  30. 30.
    Ee KC, Li PX, Balaji AK, Jawahir IS, Stevenson R (2006) Performance-based predictive models and optimization methods for turning operations and applications: part 1—tool wear/tool life in turning with coated grooved tools. J Manuf Process 8(1):54–66CrossRefGoogle Scholar
  31. 31.
    Doukas C., Stavropoulos P., Papacharalampopoulos A., Foteinopoulos P., Vasiliadis E., Chryssolouris G. (2013) “On the estimation of tool-wear for milling operations based on multisensorial data”, (CIRP CMMO) Procedia CIRP, 14th CIRP Conference on Modelling of Machining Operations, 13-14 June, Turin, ItalyGoogle Scholar
  32. 32.
    Segreto, T., Teti R. (2008) “Sensor fusion of acoustic emission and cutting force for tool wear monitoring during composite materials machining”, 6th CIRP International Conference on Intelligent Computation in Manufacturing Engineering—CIRP ICME’08, 23–25 July 2008, Naples, Italy, p. 221Google Scholar
  33. 33.
    Segreto T, Simeone A, Teti R (2012) “Sensor fusion for tool state classification in nickel superalloy high performance cutting”, 5th CIRP international conference on high performance cutting. Procedia CIRP 1:593CrossRefGoogle Scholar
  34. 34.
    Balakrishnan, P., Trabelsy, H., Kannatey-Asibu, E., Emel, E. A. (1989) “Sensor fusion approach to cutting tool monitoring”, Proc. 15th NSF Conf. on Production Research and Technology, SME, University of California, Berkeley, p. 101Google Scholar
  35. 35.
    Chiu, S.L., Morley, D.J., Martin, J. (1987) “Sensor data fusion on a parallel processor”, Proc. IEEE Int. Conf. on Robotics and Automation, Raleigh, NC, p. 1629Google Scholar
  36. 36.
    Rangwala S, Dornfeld DA (1987) “Integration of sensors via neural networks for detection of tool wear states”. Proc Winter Ann Meet ASME, PED 25:109Google Scholar
  37. 37.
    Santochi M, Dini G, Tantussi G (1997) A sensor-integrated tool for cutting force monitoring. CIRP Ann 46/1:49CrossRefGoogle Scholar
  38. 38.
    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 21:466–479CrossRefGoogle Scholar
  39. 39.
    Von Nedeß C., T. Himburg (1986) Automatisierte Uberwachung des Bohrens, VDI-Z, Bd 128 (17), 651–657Google Scholar
  40. 40.
    Tansel IN, Mekdeci C, Rodriguez O, Uragun B (1993) Monitoring drill conditions with wavelet based encoding and neural network. Int J Mach Tools Manufac 33(4):559–575CrossRefGoogle Scholar
  41. 41.
    Ertunc H. M., Loparo K.A., Ocak H. (2001) Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs), Int J Mach Tools Manufac 411363–1384Google Scholar
  42. 42.
    Rao KV, Murthy BSN, Rao NM (2014) Prediction of cutting tool wear, surface roughness and vibration of work piece in boring of AISI 316 steel with artificial neural network. Measurement 51:63–70CrossRefGoogle Scholar
  43. 43.
    Roth J. T., Pandit S. M. (1999) Monitoring end-mill wear and predicting tool failure using accelerometers. J Manufac Sci Eng Volume 121, Issue 4Google Scholar
  44. 44.
    Čuš F, Župerl U (2011) Real-time cutting tool condition monitoring in milling. Strojniški vestnik - J Mech Eng 57(2):142–150CrossRefGoogle Scholar
  45. 45.
    Zheng G., Zhao J., Li Z., Cheng X., Li L.(2014) Fractal characterization of the friction forces of a graded ceramic tool materialGoogle Scholar
  46. 46.
    Wang GF, Yang YW, Zhang YC, Xie QL (2014) Vibration sensor based tool condition monitoring using support vector machine and locality preserving projection. Sensors Actuators A 209:24–32CrossRefGoogle Scholar
  47. 47.
    FoFdation Deliverable 4.2 (May 2013) An intelligent adaptive & sustainable approach, ARTISGoogle Scholar
  48. 48.
    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 Manufac 40:599–608CrossRefGoogle Scholar
  49. 49.
    Chakraborty P, Asfour S, Cho S, Onar A, Lynn M (2008) Modeling tool wear progression by using mixed effects modeling technique when end-milling AISI 4340 steel. J Mater Process Technol 205:190–202CrossRefGoogle Scholar
  50. 50.
    Da Silva MB, Naves VTG, De Melo JDB, De Andrade CLF, Guesser WL (2011) Analysis of wear of cemented carbide cutting tools during milling operation of gray iron and compacted graphite iron. Wear 271:2426–2432CrossRefGoogle Scholar
  51. 51.
    http://www.google.com/patents/US6638609, retrieved July 15, 2014
  52. 52.
    Kalpajian S (1996) Manufacturing processes for engineering materials, 3rd edn. Addison-Wesley, LongmanGoogle Scholar
  53. 53.
    Papacharalampopoulos A, Stavropoulos P, Doukas C, Foteinopoulos P, Chryssolouris G (2013) Acoustic emission signal through turning tools: a computational study. Procedia CIRP 8(2013):426–431CrossRefGoogle Scholar
  54. 54.
    P Fromme1 and C Rouge (2011) Directivity of guided ultrasonic wave scattering at notches and cracks, Journal of Physics: Conference Series 269 012018Google Scholar
  55. 55.
    MatWeb’s searchable database of material properties, http://www.matweb.com/index.aspx
  56. 56.
    Li B, Cai H, Mao X, Huang J, Luo B (2013) Estimation of CNC machine–tool dynamic parameters based on random cutting excitation through operational modal analysis. Int J Mach Tools Manufac 71:26–40CrossRefGoogle Scholar

Copyright information

© The Author(s) 2015

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • P. Stavropoulos
    • 1
    • 2
  • A. Papacharalampopoulos
    • 1
  • E. Vasiliadis
    • 1
  • G. Chryssolouris
    • 1
  1. 1.Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and AeronauticsUniversity of PatrasPatrasGreece
  2. 2.Department of Aeronautical Studies, Hellenic Air Force AcademyDekelia Air-Force BaseAthensGreece

Personalised recommendations