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A methodology has been developed using a non-destructive ultrasonic technique for measuring surface/subsurface residual stresses in 7 mm thick AISI type 316LN stainless steel weld joints made by activated tungsten inert gas and multipass tungsten inert gas welding processes. Measurement of residual stresses using an ultrasonic technique is based on the effect of stresses on the propagation velocity of elastic waves. Critically refracted longitudinal L CR wave mode was employed and accurate transit time measurements were made across the weld joints. Quantitative values of the longitudinal residual stresses across the weld joints were estimated from the measured transit times and predetermined value of acoustoelastic constant for AISI type 316LN stainless steel. The nature of the residual stress profiles and their variations across the two types of weld joints were compared and interpreted.

DMR249A steel is indigenously developed high strength low alloy (HSLA) steel. The steel is being used for construction of Indian Aircraft Carrier and other new ships under construction at various ship yards in India. In order to enhance the depth of penetration (DOP) achievable in a single pass for gas tungsten arc welding (GTAW) process, activated fluxes were developed for the steel. The process is called activated flux gas tungsten arc welding (A-GTAW). Design of experiments (DOE) approach was employed using response surface methodology (RSM) and Taguchi technique to optimize the welding parameters for achieving maximum DOP in a single pass. Design matrix was generated using DOE techniques and bead on plate experiments were carried out to generate data for influence of welding process variables on DOP. The input variables considered were current, torch speed, and arc gap. The DOP was considered as response variable. The equations correlating DOP with the process parameters were developed for both the optimization techniques. The identified optimum process parameters were validated by carrying out bead on plate experiments. The RMS error of the predicted and measured DOP values for the validation experiments of the RSM (D-optimal) and Taguchi optimization technique was found to be 0.575 and 0.860, respectively. Thus, RSM (D-optimal) was observed to predict optimized welding process parameters for achieving maximum DOP with better accuracy during A-GTAW process. KeywordsNaval steel (DMR 249 A), activated flux gas tungsten arc welding, design of experiments, response surface methodology, Taguchi method Date

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In this article an artificial neural network based system to predict weld bead geometry using features derived from the infrared thermal video of a welding process is proposed. The multilayer perceptron and radial basis function networks are used in the prediction model and an online feature selection technique prioritises the features used in the prediction model. The efficacy of the system is demonstrated with a number of welding experiments and using the leave one out cross-validation experiments.