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In this paper, an effort is made to determine the optimized parameters in laser welding of Hastelloy C-276 using Artificial Neural Network (ANN) and Genetic Algorithm (GA). CO2 Laser welding was performed on a sheet of thickness 1.6[Formula: see text]mm based on Taguchi L27 orthogonal array. Laser power, welding speed and shielding gas flow rate were chosen as input parameters and Bead width, depth of Penetration and Microhardness were measured for assessing the weld quality. ANN was applied for modeling the welding process parameters i.e. heat input, welding speed and gas flow rate. Various learning algorithms such as Batch Back Propagation (BBP), Incremental Back Propagation (IBP), Quick Propagation (QP) and Levenberg–Marquardt (LM) were comprehensively tested for estimating the output parameters and a comparison was also made among them, with respect to prediction accuracy. BBP method was found to be the best learning algorithm. Experimental validation test was performed based on the ANN and GA predicted optimized responses and this welding input parameters provided satisfactory weld metal characteristics in terms of penetration depth, bead width and microhardness.

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In this work, an attempt is made to study the effect of heat input on mechanical and metallurgical properties of Tungsten Inert Gas (TIG) welded Hastelloy C-276 sheets. Hastelloy C-276 sheets of thickness 1.6mm were used throughout this study. Three butt joints were made by varying the values of welding current in the steps of 1 Ampere. The weld quality was assessed by measuring bead geometry, tensile strength and microhardness. From the experimental results, it was found that the hardness proportionally varied with the change in welding current whereas tensile strength first increased and then decreased with increase in welding current. The weld property variation is discussed with the aid of microstructures taken from Scanning Electron Microscope (SEM), Energy Dispersion Spectroscopy (EDS) and X-ray Diffractometer (XRD).

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