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Simultaneous optimization of burrs size and surface finish when milling 6061-T6 aluminium alloy

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International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

Abstract

Taguchi-based optimization has been successfully applied in industrial applications. Some of these applications have more than one response to study. Most of reported applications of Taguchi method deal with single objective optimization, while multiple responses optimization has received relatively less attentions. The main objective of this article is to propose new modifications to application of Taguchi method by proposing fitness mapping function (ψ) and Desirability index (Di) for correct selection of process parameters setting levels that can be used for multiple responses optimization. The proposed method is verified by simultaneous minimization of surface roughness and burrs thickness during slot milling of 6061-T6 aluminium alloy. It was found that surface roughness and burrs size can be optimized by selecting appropriate setting levels of process parameters. According to experimental results, feed per tooth has the major influence on variation of burr size and surface roughness, while cutting speed has shown less significant effect as compared to other cutting parameters used.

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Abbreviations

Yi :

Non-identical response

Ri :

Maximum value of Yi

n:

Number of responses

m :

Mean value of of responses

σ :

Standard deviation of responses

N:

Number of replications

ω:

Weighting coefficient

Mp :

Mapping function

ψ:

Fitness mapping function

MR :

Fitness mapping range

M:

The maximum value of MR

R:

Range of a response

μ:

Mapping coefficient

η:

Signal to noise ratio (SNR)

ηψ :

SNR of fitness mapping function

di :

Desirability of each response

Di :

Desirability of all transformed responses

κ:

Optimization rate

ɛ:

Prediction error

t:

Weight exponent value

F:

F-ratio

P:

P-value

DOF:

Degree of freedom

MS:

Mean of square

SS:

Sum of square

Rɛ:

Insert nose radius

D:

Tool diameter

β:

Helix angle

References

  1. Gaitonde, V. N., Karnik, S. R., and Davim, J. P., “Multiperformance optimization in turning of free-machining steel using taguchi method and utility concept,” Journal of Materials Engineering and Performance, Vol. 18, No. 3, pp. 231–236, 2009.

    Article  Google Scholar 

  2. Dhavamani, C. and Alwarsamy, T., “Review on optimization of machining operation,” International Journal of Academic Research, Vol. 3, No. 3, pp. 476–485, 2011.

    Google Scholar 

  3. Karnik, S. R., Gaitonde, V. N., and Davim, J. P., “A comparative study of the ANN and RSM modeling approaches for predicting burr size in drilling,” The International Journal of Advanced Manufacturing Technology, Vol. 38, No. 9–10, pp. 868–883, 2008.

    Article  Google Scholar 

  4. Vafaeesefat, A., “Optimum creep feed grinding process conditions for Rene 80 supper alloy using neural network,” Int. J. Precis. Eng. Manuf., Vol. 10, No. 3, pp. 5–11, 2009.

    Article  Google Scholar 

  5. Tong, K. W., Kwong, C. K., and Yu, K. M., “Process optimisation of transfer moulding for electronic packages using artificial neural networks and multiobjective optimisation techniques,” The International Journal of Advanced Manufacturing Technology, Vol. 24, No. 9–10, pp. 675–685, 2004.

    Article  Google Scholar 

  6. Lee, P. -H., Chung, H., and Lee, S. W., “Optimization of micro-grinding process with compressed air using response surface methodology,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 225, No. 11, pp. 2040–2050, 2011.

    Article  Google Scholar 

  7. Mandal, N., Doloi, B., and Mondal, B., “Force prediction model of Zirconia Toughened Alumina (ZTA) inserts in hard turning of AISI 4340 steel using response surface methodology,” Int. J. Precis. Eng. Manuf., Vol. 13, No. 9, pp. 1589–1599, 2012.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  9. Moola, M., Gorin, A., and Hossein, K., “Optimization of various cutting parameters on the surface roughness of the machinable glass ceramic with two flute square end mills of micro grain solid carbide,” Int. J. Precis. Eng. Manuf., Vol. 13, No. 9, pp. 1549–1554, 2012.

    Article  Google Scholar 

  10. Niknam, S. A., Kamguem, R., and Songmene, V., “Analysis and optimization of exit burr size and surface roughness in milling using desireability function,” Proc. of ASME 2012 International Mechanical Engineering Congress and Exposition, 2012.

    Google Scholar 

  11. Luo, M., Liu, G., and Chen, M., “Mechanism of burr formation in slot milling Al-alloy,” International Journal of Materials and Product Technology, Vol. 31, No. 1, pp. 63–71, 2008.

    Article  MathSciNet  Google Scholar 

  12. Kilickap, E., “Modeling and optimization of burr height in drilling of Al-7075 using Taguchi method and response surface methodology,” The International Journal of Advanced Manufacturing Technology, Vol. 49, No. 9–12, pp. 911–923, 2010.

    Article  Google Scholar 

  13. Songmene, V., Khettabi, R., and Kouam, J., “Dry high-speed machining: a cost effective and green process,” International Journal of Manufacturing Research, Vol. 7, No. 3, pp. 229–256, 2012.

    Article  Google Scholar 

  14. Ross, P. J., “Taguchi techniques for quality engineering: loss function, orthogonal experiments, parameter and tolerance design,” McGraw-Hill, 1988.

    Google Scholar 

  15. Wain, N., Thomas, N. R., Hickman, S., Wallbank, J., and Teer, D. G., “Performance of low-friction coatings in the dry drilling of automotive Al-Si alloys,” Surface and Coatings Technology, Vol. 200, No. 5–6, pp. 1885–1892, 2005.

    Article  Google Scholar 

  16. Zhang, J. Z., Chen, J. C., and Kirby, E. D., “Surface roughness optimization in an end-milling operation using the Taguchi design method,” Journal of Materials Processing Technology, Vol. 184, No. 1–3, pp. 233–239, 2007.

    Article  Google Scholar 

  17. Deng, C. S. and Chin, J. H., “Hole roundness in deep-hole drilling as analysed by Taguchi methods,” The International Journal of Advanced Manufacturing Technology, Vol. 25, No. 5–6, pp. 420–426, 2005.

    Article  Google Scholar 

  18. Yang, W. H. and Tarng, Y. S., “Design optimization of cutting parameters for turning operations based on the Taguchi method,” Journal of Materials Processing Technology, Vol. 84, No. 1–3, pp. 122–129, 1998.

    Article  Google Scholar 

  19. Yang, J. L. and Chen, J. C., “A systematic approach for identifying optimum surface roughness performance in end-milling operations,” Journal of Industrial Technology, Vol. 17, No. 2, pp. 2001.

  20. Ghani, J. A., Choudhury, I. A., and Hassan, H. H., “Application of Taguchi method in the optimization of end milling parameters,” Journal of Materials Processing Technology, Vol. 145, No. 1, pp. 84–92, 2004.

    Article  Google Scholar 

  21. Chen, M., Liu, G., and Shen, Z., “Study on Active Process Control of Burr Formation in Al-Alloy Milling Process,” Proc. of IEEE International Conference on Automation Science and Engineering, pp. 431–436, 2006.

    Google Scholar 

  22. Niknam, S. and Songmene, V., “Modeling of burr thickness in milling of ductile materials,” The International Journal of Advanced Manufacturing Technology, Vol. 66, No. 9–12, pp. 2029–2039, 2013.

    Article  Google Scholar 

  23. Mian, A. J., Driver, N., and Mativenga, P. T., “Estimation of minimum chip thickness in micro-milling using acoustic emission,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 225, No. 9, pp. 1535–1551, 2011.

    Article  Google Scholar 

  24. Lauderbaugh, L. K., “Analysis of the effects of process parameters on exit burrs in drilling using a combined simulation and experimental approach,” Journal of Materials Processing Technology, Vol. 209, No. 4, pp. 1909–1919, 2009.

    Article  Google Scholar 

  25. Niknam, S. A. and Songmene, V. “Statistical investigation on burrs thickness during milling of 6061-T6 aluminium alloy,” Proc. of CIRP 1st International Conference on Virtual Machining Process Technology, 2012.

    Google Scholar 

  26. S. A., Niknam., V., Songmene., “Factors governing burr formation during high-speed slot milling of wrought aluminum alloys,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture May 30, 2013, doi:10.1177/0954405413484725.

    Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, École de Technologie Supérieure (ÉTS), 1100 Notre-Dame West street, Montréal, Québec, H3C 1K3, Canada

    Seyed Ali Niknam & Victor Songmene

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  1. Seyed Ali Niknam
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  2. Victor Songmene
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Correspondence to Victor Songmene.

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Niknam, S.A., Songmene, V. Simultaneous optimization of burrs size and surface finish when milling 6061-T6 aluminium alloy. Int. J. Precis. Eng. Manuf. 14, 1311–1320 (2013). https://doi.org/10.1007/s12541-013-0178-8

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  • DOI: https://doi.org/10.1007/s12541-013-0178-8

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