Effect of welding parameters on microstructure and mechanical properties of AA7075-T6 friction stir welded joints
Bibliographic record
Abstract
The effects of advancing speed and rotational speed on the microstructure and the mechanical properties of friction stir welded 7075-T6 aluminium alloy sheets were studied. The fatigue strength of sound joints was measured and compared to tensile testing results. Macrographs and microhardness maps were carried out to reveal the microstructure transformations. Fractographic observations were made to identify the failure mechanisms. The effects of welding parameters on the fatigue strength are discussed in terms of welding pitch k (mm/rev) and heat input (J/mm). At a high welding pitch, crack initiation at the root of the circular grooves left by the tool on the weld surface is the most detrimental failure mechanism. As the size and the depth of the grooves are related to the welding pitch, the fatigue strength increases when the welding pitch is reduced. However, when the heat input is excessive, the failure is caused by sub-surface defects produced after abnormal stirring and/or by softening of the heat-affected zone. Lateral lips on the weld surface edges also have an effect on the fatigue strength for intermediate welding pitch values.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".