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

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.