Optimization of Advanced Square Wave AC-GTAW Parameters to Improve Localized Corrosion Resistance of AA6082-T651 Aluminum Welds
الموضوعات :mohammad Tabeahmadi 1 , Reza Dehmolaei 2 , Sayed Reza Alavi Zaree 3
1 - Material science and Engineering department, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 - Material science and engineering department, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
3 - Material science and engineering department, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
الکلمات المفتاحية: Taguchi Method, AA6082-T651 alloy, localized corrosion, Analysis of variance(ANOVA), regression method,
ملخص المقالة :
In this study, optimization of advanced square wave alternative current GTAW(ASW-AC-GTAW) parameters were conducted to improve localized corrosion resistance of AA6082-T651 aluminum alloy welds. To this objective, positive half cycle current(PHC), negative half cycle current(NHC), frequency(F) and positive half cycle current percentage(PHC%) were selected as main welding parameters and altered at three levels according to Taguchi method and L_9 (3^4) orthogonal array. To study the localized corrosion resistance of weld metals; potentiodynamic polarization test was performed on all samples and corresponding "Δ" "E" _"pit" (E_pit-E_corr )(mV) were measured and considered as evaluation criterion. Implementation of variance analysis(ANOVA) on measured data and "S" ⁄"N" (Signal-to-Noise) ratios indicated that the optimum levels of PHC, NHC, F, and PHC% were 300A, 190A, 2Hz, 40%, respectively. According to ANOVA of "S" ⁄"N" ratios, contribution of PHC, NHC, F, and PHC% to the results were 35.05%, 25.98%, 23.57%, and 15.27%, consecutively. Interval domain for average "Δ" "E" _"pit" calculated with 95% confidence level to be (379.4 , 386.54) (mV). confirmation sample was welded under optimum condition. The values of "Δ" "E" _"pit" of optimum sample were 381.13 and 385.47 mV. Both of this measurement fallen in the Interval domain. Therefore, the experimental results were in excellent agreement with analytical predictions. The regression model for predicting ΔE_pit values was obtained using multivariate nonlinear regression.
