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Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm

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Abstract

Modeling and optimization of cutting parameters are one of the most important elements in machining processes. The present study focused on the influence machining parameters on the surface roughness obtained in drilling of AISI 1045. The matrices of test conditions consisted of cutting speed, feed rate, and cutting environment. A mathematical prediction model of the surface roughness was developed using response surface methodology (RSM). The effects of drilling parameters on the surface roughness were evaluated and optimum machining conditions for minimizing the surface roughness were determined using RSM and genetic algorithm. As a result, the predicted and measured values were quite close, which indicates that the developed model can be effectively used to predict the surface roughness. The given model could be utilized to select the level of drilling parameters. A noticeable saving in machining time and product cost can be obtained by using this model.

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References

  1. Gaitonde VN, Karnik SR, Siddeswarapa B, Achyutha BT (2008) Integrating Box-Behnken design with genetic algorithm to determine the optimal parametric combination for minimizing burr size in drilling of AISI 316 L stainless steel. Int J Adv Manuf Technol 37:230–240

    Article  Google Scholar 

  2. Sahoo P, Barman TK, Routara BC (2008) Fractal dimension modelling of surface profile and optimisation in CNC end milling using response surface method. Int J Manuf Res 3:360–377

    Article  Google Scholar 

  3. Bhowmick S, Alpas AT (2008) Minimum quantity lubrication drilling of aluminium-silicon alloys in water using diamond-like carbon coated drills. Int J Mach Tools Manuf 48:1429–1443

    Article  Google Scholar 

  4. Nandi AK, Davim JP (2009) A study of drilling performance with minimum quantity of lubricant using fuzzy logic rules. Mechatronics 19:218–232

    Article  Google Scholar 

  5. Silva LR, Bianchi EC, Catai RE, Fusse RY, França TV, Aguiar PR (2005) Study on the behavior of the minimum quantity lubricant-MQL technique under different lubricating and cooling conditions when grinding ABNT 4340 steel. J Braz Soc Mech Sci Eng 27(2):192–199

    Google Scholar 

  6. Diniz AE, Micaroni R (2007) Influence of the direction and flow rate of the cutting fluid on tool life in turning process of AISI 1045 steel. Int J Mach Tools Manuf 47:247–254

    Article  Google Scholar 

  7. Braga DU, Diniz AE, Miranda GWA, Coppini NL (2002) Using a minimum quantity of lubricant (MQL) and a diamond coated tool in the drilling of aluminum–silicon alloys. J Mater Process Technol 122:127–138

    Article  Google Scholar 

  8. Heinemann R, Hinduja S, Barrow G, Petuelli G (2005) Effect of MSS on the tool life of small twist drills in deep-hole drilling. Int J Mach Tools Manuf 1:1–6

    Google Scholar 

  9. Zeilmann RP, Weingaertner WL (2006) Analysis of temperature during drilling of Ti6Al4V with minimal quantity of lubricant. J Mater Process Technol 179:124–127

    Article  Google Scholar 

  10. Suresh KRN, Venkateswara RP (2005) Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling. Int J Adv Manuf Technol 26:1202–1210

    Article  Google Scholar 

  11. Mital A, Mehta M (1988) Surface roughness prediction models for fine turning. Int J Produc Res 26:1861–1876

    Article  Google Scholar 

  12. Van Luttervelt CA, Childs THC, Jawahir IS, Klocke F, Venuvinod PK (1998) Present situation and future trends in modelling of machining operations. Progress Report of the CIRP Working Group on ‘Modelling of machining operations’. Ann CIRP 47(2):587–626

    Article  Google Scholar 

  13. Ozel C, Kilickap E (2006) Optimisation of surface roughness with GA approach in turning 15% SiCp reinforced AlSi7Mg2 MMC material. Int J Mach Machinability Mater 1(4):476–487

    Article  Google Scholar 

  14. Suresh PVS, Venkateswara RP, Deshmukh SG (2002) A genetic algorithmic approach for optimization of surface roughness prediction model. Int J Mach Tools Manuf 42:675–680

    Article  Google Scholar 

  15. Azouzi R, Guillot M (1998) On-line optimization of the turning using an inverse process neuro controller. J Manuf Sci Eng 120:101–107

    Article  Google Scholar 

  16. Benardos PG, Vosniakos GC (2003) Predicting surface roughness in machining: a review. Int J Mach Tools Manuf 43:833–844

    Article  Google Scholar 

  17. Myers RH, Montgomery DC (1995) Response surface methodology: process and product optimization using designed experiments. Wiley, New York

    MATH  Google Scholar 

  18. Pradhan MK, Biswas CK (2008) Modelling of machining parameters for MRR in EDM using response surface methodology. Proceedings of NCMSTA’08 Conference, Hamirpur 535–542

  19. David G (1989) Genetic algorithms in search, optimization and machine learning. Addison, Wesley

    MATH  Google Scholar 

  20. Mitsuo G, Runwei C (1997) Genetic algorithms and engineering design. Wiley-Interscience Publication

  21. Zbigniew M (1996) Genetic algorithms + data structures = evolution programs. Springer

  22. Palanikumar K (2008) Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling. Int J Adv Manuf Technol 36:19–27

    Article  Google Scholar 

  23. Palanikumar K, Karthikeyan R (2006) Optimal machining conditions for turning of particulate metal matrix composites using Taguchi and response surface methodology. Mach Sci Technol 10:417–433

    Article  Google Scholar 

  24. Noordin MY, Vankatesh VC, Sharif S, Elting S, Abdullah A (2004) Application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel. J Mater Process Technol 145:46–68

    Article  Google Scholar 

  25. Alrabii SA, Zumot LY (2007) Chip thickness and microhardness prediction models during turning of medium carbon steel. J App Math 2007:1–12

    Article  Google Scholar 

  26. Khan MMA, Dhar NR (2006) Performance evaluation of minimum quantity lubrication by vegetable oil in terms of cutting force, cutting zone temperature, tool wear, job dimension and surface finish in turning AISI-1060 steel. J Zhejiang University Science:A 7(11):1790–1799

    Article  Google Scholar 

  27. Mendes OC, Avila RF, Abrao AM, Reis P, Davim JP (2006) The performance of cutting fluids when machining aluminium alloys. Ind Lubr Tribol 58(5):260–268

    Article  Google Scholar 

  28. Tosun N, Cogun C, Tosun G (2004) A study kerf and materials removal rate in wire electrical discharge machining based on Taguchi method. J Mater Process Technol 152:316–322

    Article  Google Scholar 

  29. Davidson MJ, Balasubramanian K, Tagore GRN (2008) Surface roughness prediction of flow-formed AA 6061 alloy by design of experiments. J Mater Process Technol 202:41–46

    Article  Google Scholar 

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Correspondence to Erol Kilickap.

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Kilickap, E., Huseyinoglu, M. & Yardimeden, A. Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 52, 79–88 (2011). https://doi.org/10.1007/s00170-010-2710-7

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  • DOI: https://doi.org/10.1007/s00170-010-2710-7

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