Advertisement

Study of machinability and parametric optimization of end milling on aluminium hybrid composites using multi-objective genetic algorithm

  • B. Rajeswari
  • K. S. Amirthagadeswaran
Technical Paper
  • 98 Downloads

Abstract

Metal matrix composites offer a substantial surety to meet the present and future demands spanning from automobiles to aerospace. Hybrid metal matrix composites are a new choice of materials involving several advantages over the single reinforcement. In this present study, three specimens possessing aluminium 7075 reinforced with particulates of silicon carbide (5, 10, 15% weight percentage) and alumina (5% weight percentage) were developed using stir casting. The purpose of the study was to investigate the effect of reinforcement particles of silicon carbide on the machinability of hybrid metal matrix composites. These materials are engineered to match the requirements of optimal output responses such as low surface roughness, less tool wear, a less cutting force with the high rate of material removal under a set of practical machining constraints. Multi-objective parametric optimization using genetic algorithm obtained optimal cutting responses. The spindle speed, feed rate, depth of cut and weight percentages of SiC were selected as the influencing parameters for meeting the output responses in end milling operation. Based on the Box–Behnken design in response surface methodology, 27 experimental runs were conducted and nonlinear regression models were developed to predict the objective function. The adequacy of the model was checked through ANOVA and was found to be significant. The optimum settings of the parameters were found using multi-objective genetic algorithm. The predicted optimal settings were verified through confirmatory experiments, and the results validated.

Keywords

Composites End milling Genetic algorithm Interaction effects Multi-objective 

List of symbols

Ra

Surface roughness (µm)

MRR

Material removal rate (mm3/min)

Tw

Tool wear (mm)

Fz

Cutting force (N)

N

Spindle speed (rpm)

f

Feed rate (mm/rev)

d

Depth of cut (mm)

w

Weight percentage of silicon carbide

DF

Degree of freedom

CI

Confidence interval

VIF

Variance inflation factor

Notes

Acknowledgements

The authors thank Government College of Technology, Coimbatore, India, for funding this research work under Technical Education Quality Improvement Programme—Phase II.

References

  1. 1.
    Muthukrishnan N, Paulo Davim J (2009) Optimization of machining parameters of Al/SiC-MMC with ANOVA and ANN analysis. J Mater Process Technol 209:225–232.  https://doi.org/10.1016/j.jmatprotec.2008.01.041 CrossRefGoogle Scholar
  2. 2.
    Altunpak Y, Ay M, Aslan S (2012) Drilling of a hybrid Al/SiC/Gr metal matrix composites. Int J Adv Manuf Technol 60(5–8):513–517.  https://doi.org/10.1007/s00170-011-3644-4 CrossRefGoogle Scholar
  3. 3.
    Bhushan RK (2013) Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites. J Clean Prod 39:242–254.  https://doi.org/10.1016/j.jclepro.2012.08.008 CrossRefGoogle Scholar
  4. 4.
    Arun Premnath A, Alwarsamy T, Abhinav T, Adithya Krishnakant C (2012) Surface roughness prediction by response surface methodology in milling of hybrid aluminium composites. Procedia Eng 38:745–752.  https://doi.org/10.1016/j.proeng.2012.06.094 CrossRefGoogle Scholar
  5. 5.
    Dikshit MK, Puri AB, Maity A (2014) Experimental study of cutting forces in ball end milling of Al2014-T6 using response surface methodology. Procedia Mater Sci 6:612–622.  https://doi.org/10.1016/j.mspro.2014.07.076 CrossRefGoogle Scholar
  6. 6.
    Lebaal N, Nouari M, Ginting A (2011) A new optimization approach based on Kriging interpolation and sequential quadratic programming algorithm for end milling refractory titanium alloys. Appl Soft Comput 11:5110–5119.  https://doi.org/10.1016/j.asoc.2011.05.048 CrossRefGoogle Scholar
  7. 7.
    Arokiadass R, Palaniradja K, Alagumoorthi N (2012) Prediction and optimization of end milling process parameters of cast aluminium based MMC. Trans Nonferrous Met Soc China 22:1568–1574.  https://doi.org/10.1016/S1003-6326(11)61357-5 CrossRefGoogle Scholar
  8. 8.
    Sreenivasulu R (2013) Optimization of surface roughness and delamination damage of GFRP composite material in end milling using Taguchi design method and artificial neural network. Procedia Eng 64:785–794.  https://doi.org/10.1016/j.proeng.2013.09.154 CrossRefGoogle Scholar
  9. 9.
    Li W, Guo YB, Barkey ME, Jordon JB (2014) Effect tool wear during end milling on the surface integrity and fatigue life of inconel 718. Procedia CIRP 14:546–551.  https://doi.org/10.1016/j.procir.2014.03.056 CrossRefGoogle Scholar
  10. 10.
    Zain AM, Haron H, Sharif S (2010) Prediction of surface roughness in the end milling machining using artificial neural network. Expert Syst Appl 37:1755–1768.  https://doi.org/10.1016/j.eswa.2009.07.033 CrossRefGoogle Scholar
  11. 11.
    Yan J, Li L (2013) Multi-objective optimization of milling parameters—the trade-offs between energy, production rate and cutting quality. J Clean Prod 52:462–471.  https://doi.org/10.1016/j.jclepro.2013.02.030 CrossRefGoogle Scholar
  12. 12.
    Yusup N, Zain AM, Hashim SZM (2012) Evolutionary techniques in optimizing machining parameters—review and recent applications (2007–2011). Expert Syst Appl 39:9909–9927.  https://doi.org/10.1016/j.eswa.2012.02.109 CrossRefGoogle Scholar
  13. 13.
    Mukherjee I, Ray PK (2006) A review of optimization techniques in metal cutting processes. Comput Ind Eng 50:15–34.  https://doi.org/10.1016/j.cie.2005.10.001 CrossRefGoogle Scholar
  14. 14.
    Jawahir IS, Brinksmeier E, M’Saoubi R, Aspinwall DK, Outeiro JC, Meyer D, Umbrello D, Jayal AD (2011) Surface integrity in material removal processes: recent advances. CIRP ANN Manuf Technol 60:603–626.  https://doi.org/10.1016/j.cirp.2011.05.002 CrossRefGoogle Scholar
  15. 15.
    D’Addona DM, Teti R (2013) Genetic algorithm-based optimization of cutting parameters in turning processes. Procedia CIRP 7:323–328.  https://doi.org/10.1016/j.procir.2013.05.055 CrossRefGoogle Scholar
  16. 16.
    Senthilkumar C, Ganesan G, Karthikeyan R (2010) Bi-performance optimization of electrochemical machining characteristics of Al/20%SiCp composites using NSGA-II. Proc Inst Mech E Part B J Eng Manuf 224:1399–1407.  https://doi.org/10.1243/09544054JEM1803 CrossRefGoogle Scholar
  17. 17.
    Vijay Kumar K, Naveen Sait A, Panneerselvam K (2014) Machinability study of hybrid-polymer composite pipe using response surface methodology and genetic algorithm. J Sandw Struct Mater 16(4):418–439.  https://doi.org/10.1177/1099636214532115 CrossRefGoogle Scholar
  18. 18.
    Majumder A (2014) Comparative study of three evolutionary algorithms coupled with neural network model for optimization of electric discharge machining process parameters. Proc Inst Mech Eng Part B J Eng Manuf.  https://doi.org/10.1177/0954405414538960 Google Scholar
  19. 19.
    Shahali H, Yazdi MRS, Mohammad A, Iimanian E (2012) Optimization of surface roughness and thickness of white layer in wire electrical discharge machining of DIN 1.4542 stainless steel using micro-genetic algorithm and signal to noise ratio techniques. Proc Inst Mech Eng E Part B J Eng Manuf 226(5):803–812.  https://doi.org/10.1177/0954405411434234 CrossRefGoogle Scholar
  20. 20.
    Thangarasu VS, Devaraj G, Sivasubramanian R (2012) High speed CNC machining of AISI 304 stainless steel; Optimization of process parameters by MOGA. Int J Eng Sci Technol 4(3):66–77Google Scholar
  21. 21.
    Ganesan H, Mohankumar G (2013) Optimization of machining techniques in CNC turning centre using genetic algorithm. Arab J Sci Eng 38:1529–1538.  https://doi.org/10.1007/s13369-013-0539-8 CrossRefGoogle Scholar
  22. 22.
    Mahfouf M, Jamei M, Linkens DA (2005) Optimal design of alloy steels using multiobjective genetic algorithms. Mater Manuf Process 20:553–567.  https://doi.org/10.1081/AMP-200053580 CrossRefGoogle Scholar
  23. 23.
    Yadav RN, Yadava V, Singh GK (2014) Application of non-dominated sorting genetic algorithm for multi-objective optimization of electrical discharge diamond face grinding process. J Mech Sci Technol 28(6):2299–2306.  https://doi.org/10.1007/s12206-014-0520-9 CrossRefGoogle Scholar
  24. 24.
    Santhanakrishnan M, Sivasakthivel PS, Sudhakaran R (2015) Modeling of geometrical and machining parameters on temperature rise while machining Al 6351 using response surface methodology and genetic algorithm. J Braz Soc Mech Sci Eng 39(2):487–496.  https://doi.org/10.1007/s40430-015-0378-5 CrossRefGoogle Scholar
  25. 25.
    Malghan RL, Rao KMC, Shettigar AK, Rao SS, D’Souza RJ (2017) Application of particle swarm optimization and response surface methodology for machining parameters optimization of aluminium matrix composites in milling operation. J Braz Soc Mech Sci Eng 39(9):3541–3553.  https://doi.org/10.1007/s40430-016-0675-7 CrossRefGoogle Scholar
  26. 26.
    Prabhu S, Uma M, Vinayagam BK (2014) Electrical discharge machining parameters optimization using response surface methodology and fuzzy logic modeling. J Braz Soc Mech Sci Eng 36:637–652.  https://doi.org/10.1007/s40430-013-0112-0 CrossRefGoogle Scholar
  27. 27.
    Montgomery DC (2009) Design and analysis of experiments, 7th edn. Wiley, Singapore, pp 207–264Google Scholar
  28. 28.
    Noordin MY, Venkatesh 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 145:46–58CrossRefGoogle Scholar
  29. 29.
    Ghodsiyeh D, Golshan A, Izman S (2014) Multi-objective process optimization of wire electrical discharge machining based on response surface methodology. J Braz Soc Mech Sci Eng 36(2):310–313.  https://doi.org/10.1007/s40430-013-0079-x CrossRefGoogle Scholar
  30. 30.
    Liu J, Li J, Xu C (2014) Interaction of the cutting tools and the ceramic-reinforced metal matrix composites during micro-machining: a review. CIRP J Manuf 7(2):55–70.  https://doi.org/10.1016/j.cirpj.2014.01.003 CrossRefGoogle Scholar
  31. 31.
    Ozben T, Kilickap E, Cakır O (2008) Investigation of mechanical and machinability properties of SiC particle reinforced Al-MMC. J Mater Process Technol 198:220–225.  https://doi.org/10.1016/j.jmatprotec.2007.06.082 CrossRefGoogle Scholar
  32. 32.
    Pushpendra Bharti S, Maheshwari S, Sharma C (2012) Multi-objective optimization of electric-discharge machining process using controlled elitist NSGA-II. J Mech Sci Technol 26(6):1875–1883.  https://doi.org/10.1007/s12206-012-0411-x CrossRefGoogle Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2018

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringGovernment College of TechnologyCoimbatoreIndia
  2. 2.PrincipalUnited Institute of TechnologyCoimbatoreIndia

Personalised recommendations