Modelling of work-hardening behaviour for laser welded magnesium alloy
Bibliographic record
Abstract
Abstract To investigate the reliability of the laser welding process for the magnesium alloy ZE41A-T5, eight butt joints were welded using the same processing parameters. These joints were tensile tested in the as-welded and aged conditions and the tensile data were analyzed from work-hardening characteristics. The flow curves cannot entirely be satisfactorily described by the Kocks–Mecking model; however, the model is still applicable to the high strain zone of the flow curves where work-hardening rate decreases linearly with flow stress. The reproducibility of the initial work-hardening rate and saturation stress is statistically analyzed. The initial work-hardening rates for the base castings and welded joints vary from approximately 4000 to 7000 MPa, i. e. 1/4 to 1/3 of the base material shear modulus. The as-welded joints have slightly higher initial work-hardening rates than the base castings. Artificial aging produces lower initial work-hardening rates compared with the base material. The saturation stress ranges approximately from 260 to 320 MPa, i. e. about 2 % of the shear modulus. The saturation stress for the welded joints is lower than that for the base material. Compared with the as-weld joints, aging decreases initial work-hardening rate but slightly increases saturation stress. Both initial work-hardening rate and saturation stress become more scattered after aging.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".