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The paper presents a hybrid model of an Artificial Neural Network (ANN) and Genetic Algorithm (GA) for modeling of a hybrid laser welding process. This model is employed for the prediction and optimization of penetration depth with corresponding process parameters. A single program developed for the purpose initially establishes an optimized ANN architecture using a Back-Propagation Neural Network (BPNN), with Bayesian regularization (BR). This trained ANN is then used in conjunction with GA to find optimum parameters for the process. An experimental dataset obtained from published literature has been employed to study the effect of process input parameters (arc power, focal distance from the workpiece surface, torch angle and the distance between the laser and the welding torch.) in CO2 laser–MIG hybrid welding on the depth of penetration for 5005 Al–Mg alloy, and has been used in the present work for the purpose of training, testing and optimization of the GA–ANN model. The results indicate that the GA–ANN model can predict the output with reasonably good accuracy (mean absolute % errors of 0.7198%) and can optimize the process parameters with a negligible computational time of 100.09 s. The proposed approach is envisaged for application in a multi-variable complex problem with reasonable accuracy for prediction and optimization of operational parameters. A 4-7-1 network trained using BPNN with the BR method has been found to show the best prediction capability and a maximum penetration depth of 3.84 mm has been obtained during optimization.

A GA–ANN HYBRID MODEL FOR PREDICTION AND OPTIMIZATION OF CO2 LASER-MIG HYBRID WELDING PROCESS