Application of an optimized SA-ANN hybrid model for parametric modeling and optimization of LASOX cutting of mild steel |
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Authors: | S Chaki S Ghosal |
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Affiliation: | (1) Department of Automobile Engineering, MCKV Institute of Engineering, 243, G.T. Road (N), Liluah, Howrah, West Bengal, 711204, India;(2) Department of Mechanical Engineering, Jadavpur University, Kolkata, West Bengal, 700032, India |
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Abstract: | Laser assisted oxygen cutting (LASOX) process is an efficient method for cutting thick mild steel plates compared to conventional
laser cutting process. However, scanty information is available as to modeling of the process. The paper presents an optimized
SA-ANN model of artificial neural network (ANN) and simulated annealing (SA) to predict and optimize cutting quality of LASOX
cutting process of mild steel plates. Optimization of SA-ANN parameters is carried out first where the ANN architecture and
initial temperature for SA are optimized. The optimized ANN architecture is further trained using single hidden layer back
propagation neural network (BPNN) with Bayesian regularization (BR). The trained ANN is then used to evaluate the objective
function during optimization with SA. Experimental dataset employed for the purpose consists of input cutting parameters comprising
laser power, cutting speed, gas pressure and stand-off distance while the resulting cutting quality is represented by heat
affected zone (HAZ) width, kerf width and surface roughness. Results indicate that the SA-ANN model can predict the optimized
output with reasonably good accuracy (around 3%). The proposed approach can be extended for prediction and optimization of
operational parameters with reasonable accuracy for any experimental dataset. |
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