Optimization of Laser Cutting Parameters on 700MC Steel Using Grey Relational Analysis

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    This paper presents an investigation of the optimization of laser cutting parameters on 700MC steel. The cutting parameters investigated in this study are focused on Laser power, cutting speed, and gas pressure. Full factorial design (3k) is employed as the random run of the experimental. Grey relational analysis is used to determine the optimization of these parameters. The experimental results show that the optimal cutting condition for laser power, cutting speed and gas pressure is 2600W, 1500 mm/min and 0.06 bars, respectively. In addition, the experimental validation provided the surface roughness and kerf width is 3.870 μm and 0.696 mm respectively.

     

     


  • Keywords


    grey relational analysis; laser cutting; optimization parameter

  • References


      [1] Groover MP (2010), Fundamentals of modern manufacturing engineering material processes and system. 4th Edition, John Wiley & Sons, Inc.

      [2] Sharma A & Yadava V (2018), Experimental analysis of Nd-YAG laser cutting of sheet materials – A review. Optics and Laser Technology, Vol. 98, pp.264–280.

      [3] Dubey AK & Yadava V (2008), Laser beam machining—A review. International Journal of Machine Tools and Manufacture, 48(6), pp.609-628.

      [4] Yilbas BS, Shaukat MM & Ashraf F (2017), Laser cutting of various materials: Kerf width size analysis and life cycle assessment of cutting process. Optics and Laser Technology, Vol.93, pp.67–73.

      [5] Zhang Y & Lei J (2017), Prediction of laser cutting roughness in intelligent manufacturing mode based on ANFIS. Procedia Engineering, Vol.174, pp.82 – 89.

      [6] Fua CH, Liu JF & Guo A (2015), Statistical characteristics of surface integrity by fiber laser cutting of Nitinol vascular stents. Applied Surface Science, Vol.353, pp.291-299.

      [7] Deeying J, Asawarungsaengkul K & Chutima P (2018), Multi-objective optimization on laser solder jet bonding process in head gimbal assembly using the response surface methodology. Optics and Laser Technology, Vol.98, pp.158–168.

      [8] Kais I, Abdullah A, Abdi H, Peng LC & Yassin AW (2018), Force and temperature modelling of bone milling using artificial neural networks. Measurement, Vol.116, pp.25-37.

      [9] Ballantyne KN, Van Oorschot RA & Mitchell RJ (2008), Reduce optimization time and effort: Taguchi experimental design methods. Forensic Science International, Genetics Supplement Series Vol.1, pp.7–8.

      [10] Anand G, Satyanarayana S & Manzoor HM (2017), Optimization of process parameters in EDM with magnetic field using grey relational analysis with taguchi technique. Materials Today, Proceedings, Vol.4, pp.7723–7730.

      [11] Srirangan AK & Paulraj S (2016), Multi-response optimization of process parameters for TIG welding of Incoloy 800HT by Taguchi grey relational analysis. International Journal Engineering Science and Technology, Vol.19, No.2, pp.811-817.

      Sreenivasulu R & Srinivasa RC (2012), Application of grey relational analysis for surface roughness and roughness error in drilling of AL 6061 alloy. International Journal of Lean Thinking, Vol.3, No.2, pp.67-78

 

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Article ID: 25570
 
DOI: 10.14419/ijet.v7i4.42.25570




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