The paper presents an integrated model of artificial neural networks (ANNs) and non-dominated sorting genetic algorithm (NSGAII) for prediction and optimization of quality characteristics during pulsed Nd:YAG laser cutting of aluminium alloy. A full factorial experiment has been conducted where cutting speed, pulse energy and pulse width are considered as controllable input parameters with surface roughness and material removal rate as output to generate the dataset for the model. In ANN-NSGAII model, back propagation ANN trained with Bayesian regularization algorithm is used for prediction and computation of fitness value during NSGAII optimization. NSGAII generates complete set of optimal solution with pareto-optimal front for outputs. Prediction accuracy of ANN module is indicated by around 1.5 % low mean absolute % error. Experimental validation of B Sudipto Chaki sudipto_chaki@yahoo.co.in Ravi N. Bathe optimized output results less than 1 % error only. Characterization of the process parameters in pareto-optimal region has been explained in detail. Significance of controllable parameters of laser on outputs is also discussed.

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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 CO 2 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.