Open Access
MATEC Web Conf.
Volume 74, 2016
The 3rd International Conference on Mechanical Engineering Research (ICMER 2015)
Article Number 00002
Number of page(s) 7
Published online 29 August 2016
  1. Elzenheimer, J., Liebeck, T and Tschannerl, M. Noch viel ungenutztes Potenzial beim Bohren. Werkstatt und Betrieb. 2003; 136(11):55–57.
  2. Heinemann, R., Hinduja, S., Barrow G. Use of process signals for tool wear progression sensing in drilling small deep holes. International Journal Advance Manufacturing Technology. 2007; 33: 243–250 [CrossRef]
  3. Statistical methods for manufacturing process improvement. Retrived from: 15 April 2014
  4. Ghoreishi, M., Low, D.K.Y., Li, L. Comparative statistical analysis of hole taper and circularity in laser percussion drilling. International Journal of Machine Tools and Manufacture. 2002; 42: 985–995 [CrossRef]
  5. Kadam, M.S and Pathak, S.S. Experimental Analysis and Comparative Performance of Coated and Uncoated Twist Drill Bit Dry Machining. International Journal of Research in Mechanical Engineering and Technology. 2011; 1: 2249–5762
  6. Rao, S. and Lewlyn L.R.Dr. Impact Of Process factors On Twist Drill Wear In Machining gfrp Composites By Applying Taguchi Design Analysis And Anova Technique. International Journal of Advanced Technology & Engineering Research (IJATER). 2014; 4: 2250–3536
  7. Gaja, H. Analysis and modeling of depth-of-cut during end milling of deposited material. Masters Theses, Missouri University of Sceince and Technology. 2011.
  8. Pontuale, G., Farrelly, F.A., Petri, A., and Pitolli, L. A statistical analysis of acoustic emission signals for tool condition monitoring (TCM). Acoustics Research Letters Online. 2003; 4(1): 1529–7853 [CrossRef]
  9. xiqing, M and Chuangwen, Xu. Tool wear monitoring of acoustic emission signals from milling processes. Proceedings of the 1st International Workshop on Education Technology and Computer Science, ETCS. 2009; 1: 431–435
  10. Karali, P. Acoustic Emission based Tool Condition Monitoring System in Drilling. Proceedings of the World Congress on Engineering. 2011; 3: 2078–0958
  11. Issam, A.M. Drilling wear detection and classification using vibrations signals and artificial neural network. International Journal of Machine Tools and Manufacture. 2003; 43:707–720. [CrossRef]
  12. Jantunen, E. and Jokinen, H. Automated On-line Diagnosis of Cutting Tool Condition. International Journal of Flexible Automation and Integrated Manufacturing. 1996; 4:273–287.
  13. Sambayi, P.M.K. Drill wear monitoring using instantaneous angular speed: A comparison with conventional technology used in drill monitoring systems. Masters Theses, University of Pretoria. 2012
  14. Jie, X. Drill wear prediction and drilling conditionsrecognition with newly generated features. Ph.D Theses, Hiroshima University. 2014
  15. Heinemann, R., Hinduja, S,. Barrow, G., Petuelli, G. The Performance of Small Diameter Twist Drills in Deep-Hole Drilling. Journal of Manufacturing Science and Engineering. 2006;128.
  16. Jantunen, E. A summary of methods applied to tool condition monitoring in drilling. International Journal of Machine Tools and Manufacture. 2002; 42:997–1010. [CrossRef]
  17. Signal Processing Toolbox, For use with MATLAB User’s guide. The Math Works USA. 1998
  18. El-Wardany, T.I., Gao, D. and Elbestawi, M.A. Tool condition monitoring in drilling using vibration signature analysis. International Journal of Machine Tool and Manufacture. 1996; 36(6): 687–711 [CrossRef]