Skip to main content

Advertisement

SpringerLink
Log in

Hybrid neural network–particle swarm optimization algorithm and neural network–genetic algorithm for the optimization of quality characteristics during CO2 laser cutting of aluminium alloy

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

Aerospace and automobile industries employ aluminium alloys due to its lightweight and excellent resistance to corrosion. As aluminium is highly reflective and highly thermally conductive, it is difficult-to-cut material by laser processing. The quality characteristics of the cut predominantly depend upon the combination of laser processing parameters. The main quality indices for evaluating CO2 laser cutting were surface roughness, kerf width and kerf taper; and the machining parameters considered were laser power, speed and gas pressure. This work suggests hybrid artificial neural network (ANN)–particle swarm optimization (PSO) algorithm and artificial neural network (ANN)–genetic algorithm (GA) to optimize the associated multi-response characteristics during CO2 laser cutting of aluminium 6061 alloys. The results illustrate that the hybrid ANN–GA and ANN–PSO model is an efficient tool for the optimization of process parameters in CO2 laser cutting of difficult-to-cut material—aluminium. From the optimization results, it can be concluded that the proposed ANN–GA approach can be efficiently utilized to optimize the parameters for obtaining minimum roughness, kerf width and kerf taper.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Davis JR (1998) Aluminum and aluminum alloys. ASM International, Metals Park, pp 6–7

    Google Scholar 

  2. Ujihara K (2006) Reflectivity of metals at high temperatures. J Appl Phys 43:2376–2383

    Article  Google Scholar 

  3. Ciftci I (2006) Machining of austenitic stainless steel using CVD multi-layer coated cemented carbide tools. Tribol Int 39(6):565–569

    Article  Google Scholar 

  4. Adalarasan R, Santhanakumar M, Rajmohan M (2015) Optimization of laser cutting parameters for Al6061/SiCp/Al2O3 composite using grey based response surface methodology (GRSM). Measurement 73:596–606

    Article  Google Scholar 

  5. Dubey AK, Yadava V (2012) Laser beam machining—a review. Int J Mach Tools Manuf 48:609–628

    Article  Google Scholar 

  6. Eltawahni HA, Hagino M, Benyounis KY, Inoue T, Olabi AG (2012) Effect of CO2 laser cutting process parameters on edge quality and operating cost of AISI316L. Opt Laser Technol 44:1068–1082

    Article  Google Scholar 

  7. Stournaras P, Stavropoulos K, Salonitis G, Chryssolouris G (2009) An investigation of quality in CO2 laser cutting of aluminum. CIRP J Manuf Sci Technol 2:61–69

    Article  Google Scholar 

  8. Javidi A, Riegger U, Eichlseder W (2008) The effect of machining on the surface integrity and fatigue life. Int J Fatigue 30:2050–2055

    Article  Google Scholar 

  9. Lalwani DI, Mehta NK, Jain PK (2008) Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel. J Mater Process Technol 206:167–179

    Article  Google Scholar 

  10. Mukherjee I, Ray PK (2006) A review optimizations techniques in metal cutting processes. Comput Ind Eng 50:15–34

    Article  Google Scholar 

  11. Feng CXJ, Wang X (2002) Development of empirical models for surface roughness prediction in finish turning. Int J Adv Manuf Technol 20:348–356

    Article  Google Scholar 

  12. Zain AM, Haron H, Sharif S (2010) Prediction of surface roughness in the end milling machining using artificial neural network. J Expert Syst Appl 37:1755–1768

    Article  Google Scholar 

  13. Sharma VS, Dhiman S, Sehgal R, Sharma SK (2008) Estimation of cutting forces and surface roughness for hard turning using neural networks. J Intell Manuf 19:473–483

    Article  Google Scholar 

  14. 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 

  15. Jiao Y, Lei S, Pei ZJ, Lee ES (2004) Fuzzy adaptive net-works in machining process modeling: surface roughness prediction for turning operations. Int J Mach Tools Manuf 44:1643–1651

    Article  Google Scholar 

  16. Ozel T, Karpat Y, Figueira L, Davim JP (2007) Modeling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts. J Mater Process Technol 189:192–198

    Article  Google Scholar 

  17. Tansel IN, Ozcelik B, Bao WY, Chen P, Rincon D, Yang SY, Yenilmez A (2006) Selection of optimal cutting conditions by using GONNS. Int J Mach Tools Manuf 46:26–35

    Article  Google Scholar 

  18. Ozel T, Nadgir A (2002) Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools. Int J Mach Tools Manuf 42:287–297

    Article  Google Scholar 

  19. Tansel IN, Gulmez S, Demetgul M, Aykut S (2011) Taguchi Method-GONNS integration: complete procedure covering from experimental design to complex optimization. Expert Syst Appl 38:4780–4789

    Article  Google Scholar 

  20. Sl M, Kwong CK, Lau WS (2001) A hybrid neural network and genetic algorithm approach to the determination of initial process parameters for injection moulding. Int J Adv Manuf Technol 18(6):404–409

    Article  Google Scholar 

  21. Tansel IN, Ozcelik B, Bao WY, Chen P, Rincon D, Yang SY, Yenilmez A (2006) Selection of optimal cutting conditions by using GONNS. Int J Mach Tools Manuf 46(1):26–35

    Article  Google Scholar 

  22. Wang K, Gelgele HL, Wang Y, Yuan Q, Fang M (2003) A hybrid intelligent method for modeling the EDM process. Int J Mach Tools Manuf 43(10):995–999

    Article  Google Scholar 

  23. Ozcelik B, Oktem H, Kurtaran H (2005) Optimum surface roughness in end milling inconel 718 by coupling neural network model and genetic algorithm. Int J Adv Manuf Technol 27(3–4):234–241

    Article  Google Scholar 

  24. Pandey AK, Dubey AK (2012) Simultaneous optimization of multiple quality characteristics in laser cutting of titanium alloy sheet. Opt Laser Technol 44:1858–1865

    Article  Google Scholar 

  25. Pandey AK, Dubey AK (2013) Multiple quality optimization in laser cutting of difficult-to-laser-cut material using grey–fuzzy methodology. Int J Adv Manuf Technol 65(1–4):421–431

    Article  Google Scholar 

  26. Dubey AK, Yadava V (2008) Laser beam machining—a review. Int J Mach Tools Manuf 48:609–628

    Article  Google Scholar 

  27. Qiu P, Cui M, Kang K, Park B, Son Y, Khim E, Jang M, Khim J (2014) Application of Box–Behnken design with response surface methodology for modelling and optimizing ultrasonic oxidation of arsenite with H2O2. Cent Eur J Chem 12:164–172

    Article  Google Scholar 

  28. Anand K, Barik BK, Tamilmannan K, Sathiya P (2015) Artificial neural network modeling studies to predict the friction welding process parameters of Incoloy 800H joints. Eng Sci Technol Int J 18:394–407

    Article  Google Scholar 

  29. Adeli H, Hung SL (1994) Machine learning: neural networks, genetic algorithms, and fuzzy systems. Wiley, New York

    MATH  Google Scholar 

  30. Sardinas RQ, Santana MR, Brindis EA (2006) Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes. Eng Appl Artif Intell 19(2):127–133

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, SRM TRP Engineering College, Trichy, Tamilnadu, 621 105, India

    Senthilkumar Vagheesan

  2. Department of Mechanical Engineering, Saranathan College of Engineering, Trichy, Tamilnadu, 620 012, India

    Jayaprakash Govindarajalu

Authors
  1. Senthilkumar Vagheesan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jayaprakash Govindarajalu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Senthilkumar Vagheesan.

Additional information

Technical Editor: Lincoln Cardoso Brandao.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vagheesan, S., Govindarajalu, J. Hybrid neural network–particle swarm optimization algorithm and neural network–genetic algorithm for the optimization of quality characteristics during CO2 laser cutting of aluminium alloy. J Braz. Soc. Mech. Sci. Eng. 41, 328 (2019). https://doi.org/10.1007/s40430-019-1830-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40430-019-1830-8

Keywords

Search

Navigation