A Study on Optimization of Welding Parameters and their Effect on Joint Properties of Dissimilar AA6082-T6 and AA7050-T7 Friction Stir Welds

Abstract

This work investigates the effect of process parameters on joint properties of dissimilar AA6082-T6 and AA7050-T7 friction stir welds and determines the optimal value of process parameters using the grey-based Taguchi technique. At optimum welding parameters, similar friction stir welds of respective parent materials were developed for comparative study. Developed welds were evaluated in terms of macrostructure, microstructure, mechanical properties, and textural behaviour. Two types of plasticized material mixing patterns were observed in the weld nuggets, categorized as intercalated onion ring and interlocking lobes-based flow structures. Through electron backscattered diffraction, the fine nugget grain size was observed in dissimilar weld than weld nugget grain size of similar AA6082-T6 and AA7050-T7 welds. Unlike similar welds, the minimum hardness location lies toward the advancing side for all the dissimilar welds. Dissimilar welds' joint strength and yield strength efficiency were evaluated as 65.65% and 61.25% at optimum welding parameters, respectively. Fracture surface analysis revealed ductile behaviour in all the welded joints.

Influence of cooling environment on microstructure and mechanical properties of AA6082-T6 and AA7050-T7 dissimilar friction stir welds

“…The dissimilar friction stir welds were developed under three different cooling conditions at optimal welding parameters, 24 as mentioned in Table 2, on a computer numerical control vertical milling machine. In addition to conventional FSW, in-process cooling (i.e.…”

Optimization of parameters and formulation of numerical model employing GRA–PCA and RSM approach for friction stir welded Ti–6Al–4V alloy joints

“…This discrepancy will lead to generation of imprecise results, during the evaluation process of GRG. As a result, there prevails a need for normalizing the actual data [23,24]. The contribution percentage of each and every response can be determined by employing PCA (i.e., principal component analysis) and then used in GRG based calculation.…”