Abstract

The fabrication of dissimilar metal joints, particularly between AA 6061 aluminum alloy (Al) and AZ31B magnesium alloy (Mg), poses significant technical challenges due to their distinct metallurgical characteristics and the inherent difficulties associated with welding such materials. These challenges include the propensity for intermetallic compound formation, thermal cracking, and differences in thermal and mechanical properties between the two alloys. Cold Metal Transfer (CMT) welding, known for its low heat input and controlled metal transfer, offers a potential solution to these issues. However, optimising the process parameters to ensure strong, defect-free joints requires a systematic approach. This study aims to optimize CMT welding parameters using parametric mathematical modeling (PMM) to produce high-strength Al and Mg dissimilar joints and to study the effects of CMT parameters on tensile strength (TS) and weld metal hardness (WMH), as well as the microstructural features of AA 6061 aluminum alloy/AZ31B magnesium alloy (Al/Mg) dissimilar joints. Al/Mg dissimilar butt joints were produced by the CMT process using ER4043 as filler wire. CMT, a low-heat input welding technique, was used to mitigate issues such as intermetallic compounds (IMCs), wider heat-affected zones (HAZ), and distortion. The CMT parameters, particularly wire feed speed (WFS), welding speed (WS), and arc length correction (ALC), were optimized using response surface methodology (RSM) to maximize the TS and WMH of the Al/Mg dissimilar joints. Polynomial regression was employed to create PMMs that integrated these CMT parameters to forecast the TS and WMH of the joints. An analysis of variance (ANOVA) was applied to assess the feasibility of the PMMs. The results indicated that the Al/Mg dissimilar joints, produced using a WFS of 4700 mm/min, a WS of 280 mm/min, and an ALC of 10%, exhibited higher TS and WMH values of 33 MPa and 95.8 HV, respectively. The PMMs provided precise forecasts for the TS and WMH of the Al/Mg joints with an error rate of less than 1% and a confidence level of 97%.

Full Text
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