Unveiling the microstructure evolution and mechanical properties in a gas tungsten arc-welded Fe–Mn–Si–Cr–Ni shape memory alloy
Bibliographic record
Abstract
Abstract Fe–Mn–Si–Cr–Ni shape memory alloys (SMAs) are unique low-cost materials with shape memory properties that grant them the ability to be used in both functional and structural applications. Such SMAs are especially sought in the construction sector for the creation of new components and/or the reinforcement of damaged ones. In this study, a Fe–17Mn–5Si–10Cr–4Ni–1(V, C) wt% SMA was gas tungsten arc welded, with the objective to investigate the microstructure and mechanical performance changes occurring after welding. A comprehensive assessment of processing, microstructure and properties relationships was established combining microscopy (optical and electron), synchrotron X-ray diffraction, microhardness mapping and tensile testing including cycling assessment of the joint’s functional performance. It is shown that the present SMA has good weldability, with the joints reaching nearly 883 MPa at fracture strain of 23.6 ± 2.1%. Alongside this, several microstructure differences were encountered between the as-received and as-welded condition, including the formation of ferrite and Fe5Ni3Si2 P213 cubic precipitates amidst the fusion zone in the latter region. Graphical abstract
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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".