Welding of AA6061-T6 Sheets Using High-Strength 4xxx Fillers: Effect of Mg on Mechanical and Fatigue Properties
Bibliographic record
Abstract
Al-Si-Mg 4xxx filler metals are widely used in aluminum welding owing to their excellent weldability and capability for strength enhancement by heat treatment. However, weld joints with commercial Al-Si ER4043 fillers often exhibit poor strength and fatigue properties. In this study, two novel fillers were designed and prepared by increasing the Mg content in 4xxx filler metals, and the effects of Mg on the mechanical and fatigue properties were studied under as-welded and post-weld heat-treated (PWHT) conditions. AA6061-T6 sheets were used as the base metal and welded by gas metal arc welding. The welding defects were analyzed using X-ray radiography and optical microscopy, and the precipitates in the fusion zones were studied using transmission electron microscopy. The mechanical properties were evaluated using the microhardness, tensile, and fatigue tests. Compared to the reference ER4043 filler, the fillers with increased Mg content produced weld joints with higher microhardness and tensile strength. Joints made with fillers with high Mg contents (0.6-1.4 wt.%) displayed higher fatigue strengths and longer fatigue lives than joints made with the reference filler in both the as-welded and PWHT states. Of the joints studied, joints with the 1.4 wt.% Mg filler exhibited the highest fatigue strength and best fatigue life. The improved mechanical strength and fatigue properties of the aluminum joints were attributed to the enhanced solid-solution strengthening by solute Mg in the as-welded condition and the increased precipitation strengthening by β″ precipitates in the PWHT condition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".