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LASOX Cutting: Principles and Evolution

  • Sudipto Chaki
  • Sujit Ghosal
Chapter
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

Nowadays, gas-assisted laser cutting of mild and carbon steels has been widely used in manufacturing industries for its accuracy and efficiency. But laser power requirement increases with thickness of the plate and increases process cost substantially. Laser assisted oxygen cutting (LASOX) is an effective method for cutting thick section cutting of mild steel with low power laser. The present chapter has explained the development of the LASOX process with its basic working principle. Researches carried out so far for modelling and optimisations of the LASOX process parameters using conventional statistical methods also have been discussed. But it has been observed that, limitations of statistical methods for modelling of complex non linear relationship between variables of laser material processing can be overcome by incorporating soft computing techniques. Those techniques are widely used in different laser material Processing. Some significant recent works have been discussed in the present chapter. But it has been yet to be incorporated in LASOX processes. Chapter ends with a notion to develop constructive integrated soft computing models for modelling and optimisation of LASOX processes.

Keywords

Laser assisted oxygen cutting Modelling Optimisation Soft computing 

References

  1. Alfille JP, Pilot G, De Prunele D (1996) New pulsed YAG laser performances in cutting thick metallic materials for nuclear applications. In: Proceedings of SPIE, pp 134–144Google Scholar
  2. Arai T, Riches S (1997) Thick plate cutting with spinning laser beam. Laser Inst Am 83(1):19–26Google Scholar
  3. Canyurt OE, Kim HR, Lee KY (2008) Estimation of laser hybrid welded joint strength by using genetic algorithm approach. Mech Mater 40(10):825–831CrossRefGoogle Scholar
  4. Chaki S, Ghosal S (2011) Application of an optimised SA-ANN hybrid model for parametric modelling and optimisation of LASOX cutting of mild steel. Prod Eng Res Dev 5(3):251–262 (Springer, Heidelberg)CrossRefGoogle Scholar
  5. Chen SL (1999) The effects of high pressure assistant gas flow on high power CO2 laser cutting. J Mater Process Technol 88(1–3):57–66CrossRefGoogle Scholar
  6. Deb K (2005) Optimisation for engineering design: algorithms and examples, 8th edn. Prentice-Hall of India Private Limited, IndiaGoogle Scholar
  7. Ermolaev GV, Kovalev OB, Zaitsev AV (2013) Parameterization of hybrid laser-assisted oxygen cutting of thick steel plate. Opt Laser Technol 47:95–101CrossRefGoogle Scholar
  8. Fukaya K, Karube N (1990) Analysis of CO2 laser beam suitable for thick metal cutting. Laser Inst Am 71:61–70Google Scholar
  9. Haykin S (2006) Neural networks: a comprehensive foundation, 2nd edn. Pearson Education Inc, IndiazbMATHGoogle Scholar
  10. Kadri MB, Nisar S, Khan SZ, Khan WA (2015) Comparison of ANN and Finite Element Model for the prediction of thermal stresses in diode laser cutting of float glass. Optik—Int J Light Electron Opt 126(19):1959–1964CrossRefGoogle Scholar
  11. Kar A, Scott JE, Latham WP (1996) Theoretical and experimental studies of thick-section cutting with a chemical oxygen-iodine laser (COIL). J Laser Appl 8:125–133CrossRefGoogle Scholar
  12. Kuo C-FJ, Tsai W-L, Su T-L, Chen J-L (2011) Application of an LM-neural network for establishing a prediction system of quality characteristics for the LGP manufactured by CO2 laser. Opt Laser Technol 43(3):529–536CrossRefGoogle Scholar
  13. Luo H, Zeng H, Hu L, Hu X, Zhou Z (2005) Application of artificial neural network in laser welding defect diagnosis. J Mater Process Technol 170(1–2):403–411CrossRefGoogle Scholar
  14. Molian PA (1993) Dual-beam CO2 laser cutting of thick metallic materials. J Mater Sci 28:1738–1748CrossRefGoogle Scholar
  15. Neill WO, Gabzdyl JT (2000) New developments in oxygen-assisted laser cutting. J Opt Lasers Eng 34(4–6):355–367Google Scholar
  16. Olabi AG, Casalino G, Benyounis KY, Hashmi MSJ (2006) An ANN and Taguchi algorithms integrated approach to the optimization of CO2 laser welding. Adv Eng Softw 37(10):643–648CrossRefGoogle Scholar
  17. Park YW, Rhee S (2008) Process modeling and parameter optimization using neural network and genetic algorithms for aluminum laser welding automation. Int J Adv Manuf Technol 37:1014–1021CrossRefGoogle Scholar
  18. Steen WM (2005) Laser material processing, 3rd edn. Springer, LondonGoogle Scholar
  19. Sundar M, Nath AK, Bandyopadhyay DK, Chaudhuri SP, Dey PK, Misra D (2009) Effect of process parameters on the cutting quality in Lasox cutting of mild steel. Int J Adv Manuf Technol 40(9–10):865–874CrossRefGoogle Scholar
  20. Tsai M-J, Li C-H, Chen C-C (2008) Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm. J Mater Process Technol 208(1–3):270–283CrossRefGoogle Scholar
  21. Yilbas BS, Karatas C, Uslan I, Keles O, Usta Y, Yilbas Z, Ahsan M (2008) Wedge cutting of mild steel by CO2 laser and cut-quality assessment in relation to normal cutting. Opt Lasers Eng 46(10):777–784CrossRefGoogle Scholar
  22. Zadeh LA (1992) Fuzzy logic, neural networks and soft computing, one-page course announcement of CS 294-4, spring 1993. University of California, BerkleyGoogle Scholar
  23. Zaitsev AV, Kovalev OB, Malikov AG, Orishich AM, Shulyat’ev VB (2007) Laser cutting of thick steel sheets using supersonic oxygen jets. Quantum Electron 37:891–892CrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sudipto Chaki
    • 1
  • Sujit Ghosal
    • 2
  1. 1.Department of Automobile EngineeringMCKV Institute of EngineeringHowrahIndia
  2. 2.Department of Mechanical EngineeringNetaji Subhas Engineering CollegeKolkataIndia

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