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Multi-objective process optimization for micro-end milling of Ti-6Al-4V titanium alloy

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Abstract

Micro-end milling is one of the promising methods for rapid fabrication of features with 3D complex shapes. However, controlling the micro-end milling process to obtain the desired results is much harder compared to that of macro-end milling due to the size effect and uncontrollable factors. The problem is much pronounced when workpiece material is a difficult-to-process material such as titanium-based alloys which are widely used as material of choice for aircraft structures, turbine blades, and medical implants. In order to find the optimal process parameters which minimize the surface roughness and burr formation, experiments were conducted and models obtained with statistically based methods utilized in multi-objective particle swarm optimization to identify optimum process parameters. The results show that the average surface roughness can be minimized while burr formation is reduced concurrently.

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References

  1. Alvarez-Benitez JE, Everson RM, Fieldsend JE (2005) A MOPSO algorithm based exclusively on pareto dominance concepts, Proceedings of Third International Conference on Evolutionary MultiCriterion Optimization, Guanajuato, Mexico

  2. Aramcharoen A, Mativenga PT (2009) Size effect and tool geometry in micromilling of tool steel. Precis Eng 33(4):402–407

    Article  Google Scholar 

  3. Aurich JC, Dornfeld D, Arrazola PJ, Franke V, Leitz L, Min S (2009) Burrs-analysis, control and removal. CIRP Ann Manuf Technol 58(2):519–542

    Article  Google Scholar 

  4. Aurich JC, Dornfeld D, Arrazola PJ, Franke V, Leitz L, Min S (2009) Burrs: analysis, control and removal. CIRP Annals Manuf Technol 58:519–542

    Article  Google Scholar 

  5. Blum C, Li X (2008) Swarm intelligence in optimization. In: Blum C, Merkel D (eds) Swarm intelligence introduction and applications. Springer, Berlin, pp 58–62

    Google Scholar 

  6. Chern GL, Wu YJE, Cheng JC, Yao JC (2007) Study on burr formation in micro-machining using micro-tools fabricated by micro-EDM. Precis Eng 31(2):122–129

    Article  Google Scholar 

  7. Ciurana J, Arias G, Özel T (2009) Neural network modeling and particle swarm optimization of process parameters in pulsed laser micro-machining of hardened AISI H13 steel. Mater Manuf Process 24:358–368

    Article  Google Scholar 

  8. Dhanorker A, Özel T (2008) Meso/micro scale milling for micro-manufacturing. Int J Mechatron Manuf Syst 1(1):23–43

    Google Scholar 

  9. Eberhart RC, Shi Y (2001): Particle swarm optimization: developments, applications and resources. Proc. Congress on Evolutionary Computation 2001, Seoul, Korea. Piscataway, NJ: IEEE Service Center.

  10. Elkaseer AM, Popov K B, Negm M, Dimov SS, Minev R (2010) Material micro structure effect-based simulation model for the surface generation process in micro-milling, ICOMM/4 M 2010, April, 2010; Madison, Wisconsin.

  11. Filiz S, Conley CM, Wasserman MB, Ozdoganlar OB (2007) An experimental investigation of micro-machinability of copper 101 using tungsten carbide micro-endmills. Int J Mach Tool Manuf 47:1088–1100

    Article  Google Scholar 

  12. Hashimura M, Hassamontr J, Dornfeld DA (1999) Effect of in-plane exit angle and rake angles on burr height and thickness in face milling operation. J Manuf Sci Eng 121(1):13–19

    Article  Google Scholar 

  13. Horsch C, Schulze V, Lohe D (2006) Deburring and surface conditioning of micro milled structures by micro peening and ultrasonic wet peening. Microsyst Technol 12(7):691–696

    Article  Google Scholar 

  14. Hoyle R (2008) Developments in micro and nano engineering and manufacturing. Plast Rubber Compos 37(2):50–56

    Article  Google Scholar 

  15. Jaffery SI, Mativenga PT (2009) Assessment of the machinability of Ti-6Al-4 V alloy using the wear map approach. International Journal of Advance Manufacturing Technology 40:687–696

    Article  Google Scholar 

  16. Jin X, Altintas Y (2011) Slip-line field model of micro-cutting process with round tool edge effect. J Mater Process Technol 211(3):339–355

    Article  Google Scholar 

  17. Jun MBG, Joshi SS, Devo RE, Kapoor SG (2008) An experimental evaluation of an atomization-based cutting fluid application system for micromachining. J Manuf Sci Eng 130(3):1–8

    Article  Google Scholar 

  18. Karpat Y, Özel T (2007) Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization. Int J Adv Manuf Technol 35(3–4):234–247

    Article  Google Scholar 

  19. Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proc. IEEE Int. Conf. on Neural Networks, 4. IEEE Service Center, Piscataway, pp 1942–1948

    Google Scholar 

  20. Lee K, Dornfeld DA (2002) An experimental study on burr formation in micro milling aluminum and copper. Society of Manufacturing Engineers Technical Paper Series 30:1–8

    Google Scholar 

  21. Li H, Lai X, Li C, Feng J, Ni J (2008) Modelling and experimental analysis of the effects of tool wear, minimum chip thickness and micro tool geometry on the surface roughness in micro-end-milling. J Micromech Microeng 18(2):1–12

    Article  Google Scholar 

  22. Liow JL (2009) Mechanical micromachining: a sustainable micro-device manufacturing approach. Journal of Clean Production 17(7):662–667

    Article  Google Scholar 

  23. Lu C (2008) Study on prediction of surface quality in machining process. J Mater Process Technol 205:439–450

    Article  Google Scholar 

  24. Marcon A, Melkote S, Kalaitzidou K, DeBra D (2010) An experimental evaluation of graphite nanoplatelet based lubricant in micro-milling. CIRP Ann Manuf Technol 59(1):141–144

    Article  Google Scholar 

  25. Masuzawa T (2000) State of the art of micromachining. CIRP Ann Manuf Technol 49(2):473–488

    Article  Google Scholar 

  26. Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210

    Article  Google Scholar 

  27. Mian AJ, Driver N, Mativenga PT (2009) Micromachining of coarse-grained multi-phase material. Proceedings of the Institution of Mechanical Engineers. Journal of Engineering Manufacture: Part B 223(4):377–385

    Article  Google Scholar 

  28. Montgomery DC (2005) Design and analysis of experiment. Wiley, Hoboken

    Google Scholar 

  29. Myers RH, Montgomery DC (2002) Response surface methodology: process and product optimization using designed experiments. Wiley, Hoboken

    MATH  Google Scholar 

  30. Özel T, Liu X (2009) Investigations on mechanics based process planning of micro-end milling in machining mold cavities. Mater Manuf Process 24:1274–1281

    Article  Google Scholar 

  31. Özel T, Thepsonthi T, Ulutan D, Kaftanoğlu B (2011) Experiments and finite element simulations on micro-milling of Ti-6Al-4 V alloy with uncoated and cBN coated micro-tools, CIRP Annals- Manufacturing Technology, 60/2.

  32. Park JB, Kim YK (2000) Metallic biomaterials. In: The biomedical engineering handbook, 2nd edn. CRC Press, Boca Raton

    Google Scholar 

  33. Ramsden JJ, Allen DM, Stephenson DJ, Alcock JR, Peggs GN, Fuller G, Goch G (2007) The design and manufacture of biomedical surfaces. CIRP Ann Manuf Technol 56(2):687–711

    Article  Google Scholar 

  34. Schueler GM, Engmann J, Marx T, Haberland R, Aurich JC (2010) Burr formation and surface characteristics in micro-endmilling of titanium alloys. In: Burrs—analysis, control and removal. Springer, Berlin, pp 129–138

    Chapter  Google Scholar 

  35. Shi Y, Eberhart RC (1998) Parameter selection in particle swarm optimization. Proc. Seventh Annual Conference on Evolutionary Programming, pp. 591–601.

  36. Torres CD, Heaney PJ, Sumant AV, Hamilton MA, Carpick RW, Pfefferkorn FE (2009) Analyzing the performance of diamond-coated micro end mills. Int J Mach Tool Manuf 49:599–612

    Article  Google Scholar 

  37. Vázquez E, Ciurana J, Rodríguez CA, Thepsonthi T, Özel T (2011) Swarm intelligent selection and optimization of machining system parameters for micro-channel fabrication in medical devices. Mater Manuf Process 26(3):403–414

    Article  Google Scholar 

  38. Wang JS, Gong YD, Abba G, Chen K, Shi JS, Cai GQ (2008) Surface generation analysis in micro end-milling considering the influences of grain. Microsyst Technol 14(7):937–942

    Article  Google Scholar 

  39. Wang W, Kweon SH, Yang SH (2005) A study on roughness of the micro-end-milled surface produced by a miniatured machine tool. Journal of Material Processing Technology 162–163:702–708

    Article  Google Scholar 

  40. Weule H, Hüntrup V, Tritschler H (2001) Micro-cutting of steel to meet new requirements in miniaturization. CIRP Ann Manuf Technol 50(1):61–64

    Article  Google Scholar 

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Authors and Affiliations

  1. Manufacturing & Automation Research Laboratory, Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ, 08854, USA

    Thanongsak Thepsonthi & Tuğrul Özel

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  1. Thanongsak Thepsonthi
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  2. Tuğrul Özel
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Correspondence to Tuğrul Özel.

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Thepsonthi, T., Özel, T. Multi-objective process optimization for micro-end milling of Ti-6Al-4V titanium alloy. Int J Adv Manuf Technol 63, 903–914 (2012). https://doi.org/10.1007/s00170-012-3980-z

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  • DOI: https://doi.org/10.1007/s00170-012-3980-z

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