Skip to main content
SpringerLink
Log in

Monitoring of drill flank wear using fuzzy back-propagation neural network

  • Original Article
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The present work deals with developing a fuzzy back-propagation neural network scheme for the prediction of drill wear. Drill wear is an important issue in manufacturing industries, which not only affects the surface roughness of the hole but also influences drill life. Therefore, replacement of a drill at an appropriate time is of significant importance. Flank wear in a drill depends upon the input parameters like speed, feedrate, drill diameter, thrust force, torque and chip thickness. Therefore, it sometimes becomes difficult to have a quantitative measurement of all the parameters and a qualitative description becomes easier. For these kinds of situations, a fuzzy back-propagation neural network model was trained and was shown to predict drill wear with reasonable accuracy. In the present case, a left and right (LR)-type fuzzy neuron was used. The proposed model is composed of various modules like fuzzy data collection at input fuzzy neuron, defuzzification of input data to get output, calculation of mean square error (MSE), and feedback to update the network. Results show a very good prediction of drill wear from the fuzzy back-propagation neural network model.

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

Similar content being viewed by others

References

  1. Lin SC, Ting CJ (1995) Tool wear monitoring in drilling using force signals. J Wear 180:53–60

    Article  Google Scholar 

  2. Lin SC, Ting CJ (1996) Drill wear monitoring using neural network. Int J Mach Tools Manuf 36:465–475

    Article  Google Scholar 

  3. Lee BY, Liu HS, Tarng YS (1998) Modeling and optimization of drilling process. J Mater Process Technol 74:149–157

    Article  Google Scholar 

  4. Xiaoli Li, Tso SK (1999) Drill wear monitoring based on current signals. J Wear 231:172–178

    Article  Google Scholar 

  5. Tsao CC (2002) Prediction of flank wear of different coated drills for JIS SUS 304 stainless steel using neural network. J Mater Process Technol 123:354–360

    Article  Google Scholar 

  6. Ertunc HM, Loparo KA (2001) A decision fusion algorithm for tool wear condition monitoring in drilling. Int J Mach Tools Manuf 41:1347–1362

    Article  Google Scholar 

  7. Abbu-Mahfouz I (2003) Drilling wear detection and classification using vibration signals and artificial neural network. Int J Mach Tools Manuf 43:707–720

    Article  Google Scholar 

  8. Kim D, Ramulu M (2004) Drilling process optimization for graphite/bismaleimide-titanium alloy stack. J Compos Struct 63:101–114

    Article  Google Scholar 

  9. Singh AK, Panda SS, Pal SK, Chakraborty D (2006) Predicting drill wear using an artificial neural network. Int J Adv Manuf Tech 28:456–462

    Article  Google Scholar 

  10. Li X, Dong S, Venuvinod PK (2000) Hybrid learning for tool wear monitoring. Int J Adv Manuf Tech 16:303–307

    Article  Google Scholar 

  11. Kuo RJ, Cohen PH (1999) Multi-sensor integration for online tool wear estimation through radial basis function networks and fuzzy neural network. Neural Netw 12:355–370

    Article  Google Scholar 

  12. Lo S-P (2002) The application of an ANFIS and Grey system method in turning tool-failure detection. Int J Adv Manuf Tech 19:564–572

    Article  Google Scholar 

  13. Hashmi K, Graham ID, Mills B (2000) Fuzzy logic based data selection for drilling process. J Mater Process Technol 108:55–61

    Article  Google Scholar 

  14. Ghasempoor A, Jeswiet J, Moore TN (1999) Real-time implementation of on-line tool condition monitoring in turning. Int J Mach Tools Manuf 39:1883–1902

    Article  Google Scholar 

  15. Ezugwu EO, Fadare DA, Bonney J, Da Silva RB, Sales WF (2005) Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network. Int J Mach Tools Manuf 45:1375–1385

    Article  Google Scholar 

  16. Kuo RJ (2000) Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuzzy neural network. Eng Appl Artif Intell 13:249–261

    Article  Google Scholar 

  17. Freeman JA, Skapura DM (2004) Neural networks algorithm, application, and programming techniques. Pearson Education, Upper Saddle River, NJ, USA

  18. Haykin S (2004) Neural networks a comprehensive foundation, 2nd edn. Prentice Hall, NJ, USA

Download references

Author information

Authors and Affiliations

  1. Mechanical Engineering Department, Indian Institute of Technology Guwahati, Guwahati, 781 039, India

    S. S. Panda & D. Chakraborty

  2. Mechanical Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, 721 302, India

    S. K. Pal

Authors
  1. S. S. Panda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. K. Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Chakraborty.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Panda, S.S., Chakraborty, D. & Pal, S.K. Monitoring of drill flank wear using fuzzy back-propagation neural network. Int J Adv Manuf Technol 34, 227–235 (2007). https://doi.org/10.1007/s00170-006-0589-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-006-0589-0

Keywords

Search

Navigation