Microstructure and fatigue properties of 780SF steel welded joints: Effect of filler metals
Bibliographic record
Abstract
The aim of this study was to identify the effect of filler metals (low-strength MG50T and high-strength YM80A alloys) on the microstructure and mechanical properties of 780SF steel joints made via metal active gas (MAG) welding. While 780SF-MG50T joint showed mainly pearlite and ferrite in the fusion zone (FZ), martensite plus acicular ferrite was observed in the FZ of 780SF-YM80A joint. In both types of welded joints, no unfavorable soft zones were present, unlike the same grade DP780 steel. FZ and heat-affected zone (HAZ) were detailed into two subzones due to the presence of different microstructures which were governed by the temperature and equivalent carbon content. A “suspension bridge”-like hardness profile was present in the 780SF-MG50T joint owing to the formation of localized martensite at the FZ/HAZ interface, while a “bell dome”-like hardness profile was obtained for the 780SF-YM80A joint due to the presence of martensite in the FZ. A joint efficiency of 100% was achieved for the 780SF-YM80A joint with base steel failure and intact weld remained after tensile testing, while it was 93% for the 780SF-MG50T joint with failure from the weld root. The 780SF-YM80A joint with a higher tensile strength had a longer fatigue life at higher cyclic stress amplitudes, but a shorter fatigue life at lower stress amplitudes than the 780SF-MG50T joint. The fatigue life in both joints followed Basquin’s equation nicely. All the fatigued samples failed exclusively at the weld root due to the overwhelming effect of stress concentration and tensile residual stresses. The results revealed the major implications of fatigue of welded joints under dynamic loading in guaranteeing their safe and reliable structural applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".