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This paper tends to discuss the relation between welding parameters (Voltage, ArcCurrent and Travel Speed) and the deposited lines shape (width, height andpenetration) in metal deposition process. Due to the high nonlinearity of the processANN (artificial neural network) was found to be the best chose for representing it.ANN is trained off-line under different operating conditions then used for prediction ofthe system model for further optimization of the process. The results show thecapability of the developed ANNs to represent the process properly. Supervisedlearning with back propagation technique was used to make the networks. Bestnetwork for width consisted of tansig functions with an output layer of linear function.Traingd function gave the best result for width. Best network for penetration consistedof tansig functions with an output layer of linear function. Traingd function gave thebest result for penetration. Best network for height consisted of mixture of radialbasis, tansig functions with an output layer of linear function. Traingd function gavethe best result for height.

MODELING OF THE WELD BEAD SHAPE AT DIFFERENT WELDING PARAMETERS