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Correlation of the holes quality with the force signals in a microdrilling process of a sintered tungsten-copper alloy

  • Gerardo BeruvidesEmail author
  • Ramón Quiza
  • Raúl del Toro
  • Fernando Castaño
  • Rodolfo E. Haber
Article

Abstract

Holes quality errors are an undesired but unavoidable consequence in drilling operations. Due to the small dimensions involved in the microdrilling processes, quality measurement and control must be carried out offline, by using microscopy or other high precision measurement devices. This paper presents a study about the correlation between the holes quality and the force signals in the microdilling process of 0.1 mm and 0.5 mm-diameter holes in a sintered tungsten-copper alloy. The surface of the obtained holes was scanned by means of an interferometry microscope and the error of the holes was computed from the scanned data. The three components of the forces were measured during all the drilling process. The behavior of these signals, in three different intervals (tool entrance, forward motion and backward motion) was described by wavelet package analysis. The features having higher correlation with the holes quality error were the average power of the axial component of the forces in the frequency bands of 0~391 Hz and 3906~4297 Hz, during the backward motion. With these features, a statistical regression model was fitted. The main outcomes of this study are the basement for obtaining reliable models for monitoring systems in microdrilling operations.

Keywords

Holes quality errors Microdrilling Modeling Signal feature extraction Wavelet package analysis 

Nomenclature

Fx

x-component of the force

Fy

y-component of the force

Fz

z-component of the force

θ

overall angle of the section

ϕ

angular step

D

diameter of the circumference

xi

x-coordinate of the boundary points

yi

y-coordinate of the boundary points

R

radius of the circumference

ε

holes quality error

R2

coefficient of determination

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References

  1. 1.
    Sardinas, R. Q., Reis, P., and Davim, J. P., “Multi-Objective Optimization of Cutting Parameters for Drilling Laminate Composite Materials by using Genetic Algorithms,” Composites Science and Technology, Vol. 66, No. 15, pp. 3083–3088, 2006.CrossRefGoogle Scholar
  2. 2.
    Pawade, R., Joshi, S. S., and Brahmankar, P., “Effect of Machining Parameters and Cutting Edge Geometry on Surface Integrity of High-Speed Turned Inconel 718,” International Journal of Machine Tools and Manufacture, Vol. 48, No. 1, pp. 15–28, 2008.CrossRefGoogle Scholar
  3. 3.
    Watanabe, H., Tsuzaka, H., and Masuda, M., “Microdrilling for Printed Circuit Boards (PCBs)-Influence of Radial Run-Out of Microdrills on Hole Quality,” Precision Engineering, Vol. 32, No. 4, pp. 329–335, 2008.CrossRefGoogle Scholar
  4. 4.
    Rahman, A. A., Mamat, A., and Wagiman, A., “Effect of Machining Parameters on Hole Quality of Micro Drilling for Brass,” Modern Applied Science, Vol. 3, No. 5, pp. p221, 2009.CrossRefGoogle Scholar
  5. 5.
    Kumar, B. S. and Baskar, N., “Integration Of Fuzzy Logic with Response Surface Methodology for Thrust Force and Surface Roughness Modeling of Drilling on Titanium Alloy,” The International Journal of Advanced Manufacturing Technology, Vol. 65, No. 9–12, pp. 1501–1514, 2013.CrossRefGoogle Scholar
  6. 6.
    Xu, J., An, Q., Cai, X., and Chen, M., “Drilling Machinability Evaluation on New Developed High-Strength T800S/250F CFRP Laminates,” Int. J. Precis. Eng. Manuf., Vol. 14, No. 10, pp. 1687–1696, 2013.CrossRefGoogle Scholar
  7. 7.
    Chandrasekaran, M., Muralidhar, M., Krishna, C. M., and Dixit, U., “Application of Soft Computing Techniques in Machining Performance Prediction and Optimization: a Literature Review,” The International Journal of Advanced Manufacturing Technology, Vol. 46, No. 5–8, pp. 445–464, 2010.CrossRefGoogle Scholar
  8. 8.
    Yang, R. T., Liao, H. T., Yang, Y. K., and Lin, S. S., “Modeling and Optimization in Precise Boring Processes for Aluminum Alloy 6061T6 Components,” Int. J. Precis. Eng. Manuf., Vol. 13, No. 1, pp. 11–16, 2012.CrossRefGoogle Scholar
  9. 9.
    Lee, S. S. and Chen, J. C., “On-Line Surface Roughness Recognition System using Artificial Neural Networks System in Turning Operations,” The International Journal of Advanced Manufacturing Technology, Vol. 22, No. 7–8, pp. 498–509, 2003.CrossRefGoogle Scholar
  10. 10.
    Lu, C., “Study on Prediction of Surface Quality in Machining Process,” Journal of Materials Processing Technology, Vol. 205, No. 1, pp. 439–450, 2008.CrossRefGoogle Scholar
  11. 11.
    Zuperl, U., Cus, F., and Reibenschuh, M., “Neural Control Strategy of Constant Cutting Force System in End Milling,” Robotics and Computer-Integrated Manufacturing, Vol. 27, No. 3, pp. 485–493, 2011.CrossRefGoogle Scholar
  12. 12.
    Kim, D. W., Lee, Y. S., Park, M. S., and Chu, C. N., “Tool Life Improvement by Peck Drilling and Thrust Force Monitoring during Deep-Micro-Hole Drilling of Steel,” International Journal of Machine Tools and Manufacture, Vol. 49, No. 3, pp. 246–255, 2009.CrossRefGoogle Scholar
  13. 13.
    Niknam, S. A. and Songmene, V., “Simultaneous Optimization of Burrs Size and Surface Finish when Milling 6061-T6 Aluminium Alloy,” Int. J. Precis. Eng. Manuf., Vol. 14, No. 8, pp. 1311–1320, 2013.CrossRefGoogle Scholar
  14. 14.
    Uhlmann, E., Piltz, S., and Schauer, K., “Micro Milling of Sintered Tungsten-Copper Composite Materials,” Journal of Materials Processing Technology, Vol. 167, No. 2, pp. 402–407, 2005.CrossRefGoogle Scholar
  15. 15.
    Shibuya, N., Ito, Y., and Natsu, W., “Electrochemical Machining of Tungsten Carbide Alloy Micro-Pin with NaNO3 Solution,” Int. J. Precis. Eng. Manuf., Vol. 13, No. 11, pp. 2075–2078, 2012.CrossRefGoogle Scholar
  16. 16.
    Cross, K. J., McBride, J. W., and Lifton, J. J., “The Uncertainty of Radius Estimation in Least-Squares Sphere-Fitting, with an Introduction to a New Summation based Method,” Precision Engineering, Vol. 38, No. 3, pp. 499–505, 2014.CrossRefGoogle Scholar
  17. 17.
    Berman, M., “Large Sample Bias in Least Squares Estimators of a Circular Arc Center and Its Radius,” Computer Vision, Graphics, and Image Processing, Vol. 45, No. 1, pp. 126–128, 1989.CrossRefGoogle Scholar
  18. 18.
    Nievergelt, Y., “Computing Circles and Spheres of Arithmitic Least Squares,” Computer Physics Communications, Vol. 81, No. 3, pp. 343–350, 1994.zbMATHMathSciNetCrossRefGoogle Scholar
  19. 19.
    Sui, W. and Zhang, D., “Four Methods for Roundness Evaluation,” Physics Procedia, Vol. 24, pp. 2159–2164, 2012.CrossRefGoogle Scholar

Copyright information

© Korean Society for Precision Engineering and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Gerardo Beruvides
    • 1
    • 3
    Email author
  • Ramón Quiza
    • 1
  • Raúl del Toro
    • 3
  • Fernando Castaño
    • 3
  • Rodolfo E. Haber
    • 2
    • 3
  1. 1.Department of Mechanical EngineeringUniversity of MatanzasMatanzasCuba
  2. 2.Department of Informatics EngineeringAutonomous University of MadridMadridSpain
  3. 3.Center for Automation and RoboticMadridSpain

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