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Constrained grinding optimization for time, cost, and surface roughness using NSGA-II

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Abstract

Selection of parameters in machining process significantly affects quality, productivity, and cost of a component. This paper presents an optimization procedure to determine the optimal values of wheel speed, workpiece speed, and depth of cut in a grinding process considering certain grinding conditions. Experimental studies have been carried out to obtain optimum conditions. Mathematical models have also been developed for estimating the surface roughness based on experimental investigations. A non-dominated sorting genetic algorithm (NSGA II) is then used to solve this multi-objective optimization problem. The objectives under investigation in this study are surface finish, total grinding time, and production cost subjected to the constraints of production rate and wheel wear parameters. The Pareto-optimal fronts provide a wide range of trade-off operating conditions which an appropriate operating point can be selected by a decision maker. The results show the proposed algorithm demonstrates applicability of machining optimization considering conflicting objectives.

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References

  1. Lee K, Hsu MR, Chou AJH, Guo CY (2011) Improved differential evolution approach for optimization of surface grinding process. Expert Syst Appl 38:5680–5686

    Article  Google Scholar 

  2. Ben Fredj N, Amamou R (2006) Ground surface roughness prediction based upon experimental design and neural network models. Int J Adv Manuf Technol 31:24–36

    Article  Google Scholar 

  3. Gupta R, Shishodia KS (2001) Optimization of grinding process parameters using enumeration method. J Mater Process Technol 112:63–67

    Article  Google Scholar 

  4. Govindhasamy JJ, McLoone SF, Irwin GW, French JJ, Doyle RP (2005) Neural modelling, control and optimisation of an industrial grinding process. Control Eng Pract 13:1243–1258

    Article  Google Scholar 

  5. Li GF, Wang SL, Yang LB (2002) Multi-parameter optimization and control of the cylindrical grinding process. J Mater Process Technol 129:232–236

    Article  Google Scholar 

  6. KwakJ S (2005) Application of Taguchi and response surface methodologies for geometric error in surface grinding process. Int J Mach Tools Manuf 45:327–334

    Article  Google Scholar 

  7. Suresh PVS, Venkateswara Rao P, 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 

  8. Pawar PJ, Venkata Rao R (2012) “Parameter optimization of machining processes using teaching–learning-based optimization algorithm,” Int J Adv Manuf Technol

  9. Wen MX, Tay AAM, Nee AAN (1992) Micro-computer-based optimization of the surface grinding process. J Mater Process Technol 29:75–90

    Article  Google Scholar 

  10. Saravanan R, Asokan P, Sachidanandam M (2002) A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations. Int J Mach Tools Manuf 42:1327–1334

    Article  Google Scholar 

  11. Saravanan R, Sachithanandam M (2001) Genetic algorithm (GA) for multivariable surface grinding process optimisation using a multi-objective function model. Int J Adv Manuf Technol 17:330–338

    Article  Google Scholar 

  12. Gopala Krishna A, Mallikarjuna Rao K (2006) Multi-objective optimisation of surface grinding operations using scatter search approach. Int J Adv Manuf Technol 29:475–480

    Article  Google Scholar 

  13. Baskar N, Saravanan R, Asokan P, Prabhaharan G (2004) Ants colony algorithm approach for multi-objective optimisation of surface grinding operations. Int J Adv Manuf Technol 23:311–317

    Article  Google Scholar 

  14. Gopala Krishna A (2007) Optimization of surface grinding operations using a differential evolution approach. J Mater Process Technol 183:202–209

    Article  Google Scholar 

  15. Slowik A, Slowik J (2008) Multi-objective optimization of surface grinding process with the use of evolutionary algorithm with remembered Pareto set. Int J Adv Manuf Technol 37:657–669

    Article  Google Scholar 

  16. Shaji S, Radhakrishnan V (2003) Analysis of process parameters in surface grinding with graphite as lubricant based on the Taguchi method. J Mater Process Technol 141:51–59

    Article  Google Scholar 

  17. Venu Gopal A, Venkateswara Rao P (2003) Selection of optimum conditions for maximum material removal rate with surface finish and damage as constraints in SiC grinding. Int J Mach Tools Manuf 43:1327–1336

    Article  Google Scholar 

  18. Fetecau C, Stan F (2012) Study of cutting force and surface roughness in the turning of polytetrafluoroethylene composites with a polycrystalline diamond tool. Measurement 45:1367–1379

    Article  Google Scholar 

  19. Durairaja M, Gowri S (2013) Parametric optimization for improved tool life and surface finish in micro turning using genetic algorithm. Int Conf Des Manuf IConDM 64:878–887

    Google Scholar 

  20. Zain AM, Haron H, Sharif S (2010) Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Syst Appl 37:4650–4659

    Article  Google Scholar 

  21. Daddona DM, Teti R (2013)“Genetic algorithm-based optimization of cutting parameters in turning processes”, Forty Sixth CIRP Conference on Manufacturing Systems,7,p.323-328

  22. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran

    Mohammad Hadi Gholami & Mahmood Reza Azizi

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  1. Mohammad Hadi Gholami
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  2. Mahmood Reza Azizi
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Correspondence to Mohammad Hadi Gholami.

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Gholami, M.H., Azizi, M.R. Constrained grinding optimization for time, cost, and surface roughness using NSGA-II. Int J Adv Manuf Technol 73, 981–988 (2014). https://doi.org/10.1007/s00170-014-5884-6

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  • DOI: https://doi.org/10.1007/s00170-014-5884-6

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