Advertisement

Modelling and Optimisation of LASOX Cutting of Mild Steel: A Case Study

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

Abstract

Integrated ANN-GA and ANN-SA methodology are two integrated soft computing based models that can predict and optimise  input-output parameters of any manufacturing process with required optimisation accuracy eliminating the need of any closed form objective functions. In the present chapter, a case study on modelling and optimisation of CO2 LASOX cutting of mild steel plates have been carried out using the integrated ANN-GA and ANN-SA methodology to investigate efficacy of those integrated methods. In the case study cutting speed, gas pressure, laser power and standoff distance have been considered as process variables for modelling and optimisation of HAZ width, kerf width and surface roughness. 36 different ANNs have been trained and tested for ANN modelling. Finally, 4-8-3 network during training and testing through BPNN with BR results best prediction performance with MSE of 8.63E−04. Prediction capability of the best ANN (4-8-3) is found superior compared to the second order regression models developed for the purpose and is used in integrated models for optimisations. During optimisation, ANN-SA is found to show best optimisation performance with maximum absolute % error of 5.18% during experimental validation. Optimum cut quality is produced by low gas pressure and high cutting speed, laser power and stand off distance.

Keywords

CO2 LASOX cutting Modelling Optimisation Artificial neural networks (ANN) Genetic algorithms (GA) Simulated annealing (SA) Integrated ANN-GA methodology Integrated ANN-SA methodology 

References

  1. Chaki S, Ghosal S (2010) Prediction of cutting quality in LASOX cutting of mild steel using ANN and regression model. In: Proceedings of National Conference on Recent Advances in Manufacturing Technology and Management (RAMTM2010), Jadavpur University, Kolkata, 19–20 Feb, pp 163–168Google Scholar
  2. Neill WO, Gabzdyl JT (2000) New developments in oxygen-assisted laser cutting. J Opt Lasers Eng 34(4–6):355–367Google Scholar
  3. 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

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

Personalised recommendations