Improving tensile properties by varying the welding conditions of the passes of the double-sided friction stir welding of AZ31B magnesium alloy
Bibliographic record
Abstract
The present study aims to improve the tensile properties of the double-sided friction stir welded joints of AZ31B magnesium alloy by simultaneously varying the process parameters of the 1st and 2nd passes based on the design matrix developed using response surface methodology . The twenty numbers of welded joints fabricated as per the design matrix were evaluated for tensile strength , followed by the microstructural characterization, microhardness evaluation, and fractographic analysis of the optimized welded joint . A strong interaction among the rotational speed of the 1st and 2nd passes was noticed on the ultimate tensile strength . The EBSD analysis of the optimized welded joint revealed that the mutual variation of the process parameters for 1st and 2nd passes led to the inhomogeneous distribution of fine and ultra-fine grains within the overlap region (OR). The extensive grain refinement in the OR compared to the stir zones (SZ) of 1st and 2nd passes significantly improved the microhardness of the joint, followed by the attainment of ≈ 84 % joint efficiency and ∼ 12 % percentage elongation. The fractographic analysis of the fractured tensile specimen revealed the mixed brittle and ductile fracture morphology for the DS-FSW joint as compared to the dominating ductile fracture behavior for the base material. The abnormal texture variation introduced within the interface of the OR and SZ of the 1st pass formed the weakest bonded region of the optimized DS-FSW joint.
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 | 0.000 |
| 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".