Multi-objective Optimization and Experimental Investigation of CNC Oxy-Fuel Gas Cutting Parameters Using Taguchi Coupled Data Envelopment Analysis

  • Dilip Kumar BagalEmail author
  • Ajit Kumar Pattanaik
  • Dulu Patnaik
  • Abhishek Barua
  • Siddharth Jeet
  • Surya Narayan Panda
Conference paper
Part of the Lecture Notes in Intelligent Transportation and Infrastructure book series (LNITI)


An optimized design of the various machining parameters for the CNC oxy-fuel gas cutting process on SAE/AISI–4140 steel has been carried out by using DEA-based Taguchi methodology. SAE/AISI–4140 steel or Chromoly steel has high fatigue strength, impact resistance, durability, and noble ductile properties. The main output responses are bevel angle, dross breadth, and dross height, and the input parameters are nozzle speed, oxy-fuel speed, and torch height. Nine experiments were piloted based on a L9 orthogonal array of Taguchi design. The relative efficiency was determined from data envelopment analysis (DEA) method with Lingo version 14 software package. These scores were significantly affected by the machining parameters of oxy-fuel gas cutting process directly.


CNC oxy-fuel gas cutting SAE/AISI–4140 steel DEA Taguchi methodology Relative efficiency 



This research work is jointly supported by National Institute of Technology, Rourkela, India and Government College of Engineering Kalahandi, Bhawanipatna, Odisha, India.


  1. 1.
    Vora FR, Trivedi JH (2011) CNC profile gas cutting machine—application with nesting software and computer aided programming mechanism. In: National conference on recent trends in engineering & technology, pp 1–4Google Scholar
  2. 2.
    Zhou B, Liu Y-J, Tan S-K (2013) Efficient simulation of oxygen cutting using a composite heat source model. Int J Heat Mass Transf 57:304–311CrossRefGoogle Scholar
  3. 3.
    Chen S-L (1999) The effects of high-pressure assistant-gas flow on high-power CO2 laser cutting. J Mater Process Technol 88:57–66CrossRefGoogle Scholar
  4. 4.
    Chen S-L (1998) The effects of gas composition on the CO2 laser cutting of mild steel. J Mater Process Technol 73:147–159CrossRefGoogle Scholar
  5. 5.
    Maity KP, Bagal DK (2015) Effect of process parameters on cut quality of stainless steel of plasma arc cutting using hybrid approach. Int J Adv Manuf Technol 78:161–175CrossRefGoogle Scholar
  6. 6.
    Kumar A, Sahu J, Datta S, Mahapatra SS (2012) DEA based taguchi approach for multi-objective optimization in machining polymers: a case studyGoogle Scholar
  7. 7.
    Madić MJ, Radovanović MR (2013) Identification of the robust conditions for minimization of the HAZ and burr in CO2 laser cutting. FME Trans 41:130–137 Google Scholar
  8. 8.
    Ahmadi B, Torkamany M, Jaleh B, Sabaghzadeh J (2009) Theoretical comparison of oxygen assisted cutting by CO2 and Yb: YAG fiber lasers. Chin J Phys 47:465–475Google Scholar
  9. 9.
    Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444MathSciNetCrossRefGoogle Scholar
  10. 10.
    Adler N, Friedman L, Sinuany-Stern Z (2002) Review of ranking methods in the data envelopment analysis context. Eur J Oper Res 140:249–265MathSciNetCrossRefGoogle Scholar
  11. 11.
    Lanyi M (2000) Discussion on steel burning in oxygen (from a steelmaking metallurgist’s perspective). In: Ninth international symposium on flammability and sensitivity of materials in oxygen-enriched atmospheres, pp 163–178Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Dilip Kumar Bagal
    • 1
    Email author
  • Ajit Kumar Pattanaik
    • 1
  • Dulu Patnaik
    • 1
  • Abhishek Barua
    • 2
  • Siddharth Jeet
    • 2
  • Surya Narayan Panda
    • 3
  1. 1.Government College of EngineeringKalahandi, BhawanipatnaIndia
  2. 2.Centre for Advanced Post Graduate StudiesBPUTRourkelaIndia
  3. 3.Birsa Institute of Technology SindriDhanbadIndia

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