Evaluation of hot cracking susceptibility of aluminium alloy AA7075 during laser welding
Bibliographic record
Abstract
Laser welding technology on aluminum alloy 7075 is used increasingly in the automotive industry and aerospace applications. However, the use of this method still needs to improve due to the loss of mechanical properties or to the presence of defects, especially regarding hot cracking. The AA7075 have a significant tendency to crack formation because of their large solidification temperature range and shrinkage, which it is sensitive to thermal influences. The present work is concerned with the influence of the welding parameters and filler wire composition as a function of microcracks on the crack formation during laser welding, as well as observing of hot cracking and its propagation path to obtain a weld joint free of cracks. Non-destructive testing (NOT) technique with X-ray test has been applied for detecting cracks in the different welds of CPT samples. An optical microscope was used to characterize and investigate the microstructure. Microhardness test has been conducted for the same specimens to characterize the mechanical properties. Moreover, the local deformation in the fusion zone and its surrounding was investigated using the micro-flat tensile test with digital image correlation (DIC). The microstructure observation indicated that solidification cracking was formed at the grain boundary due to a proper amount of low melting point eutectic liquid phases during solidification after the laser welding.
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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".