Transitioning solidification mode via electroplated Ni coatings in martensitic stainless steel resistance spot welds: new insights into fabricating tough microstructure
Bibliographic record
Abstract
The present study addresses the enhancement of fracture toughness of martensitic stainless steel (MSS) spot welds by utilizing through electroplating of Ni on MSS sheets. The equilibrium and non-equilibrium solidification modelling showed that by Ni coating with 50 μm thick on 1.5 mm thick MSSs, the solidification mode changes from δ-ferrite to γ-austenite, leading to a weld nugget (WN) dominated by austenite grains. Moreover, electron backscatter diffraction (EBSD) and electron probe microanalysis (EPMA) showed that the other phases (martensite, δ-ferrite) appeared in band areas of WN owing to incomplete mixing of MSS and the Ni-coating. The tough microstructure in the Ni-coated MSS spot welds provided superior mechanical properties compared to non-coated welds, both in cross-tension (CT) and tensile-shear (TS) tests. Notably, the TS and CT strengths of the Ni-coated MSS spot welds showed a remarkable increase of 57% and 127%, respectively, in comparison to the conventional bare MSS spot welds. Furthermore, in terms of failure energy, the Ni-coated MSS spot welds demonstrated a substantial enhancement of 296% in TS and 520% in CT, when compared to their non-coated counterparts. This research study showcased the effectiveness of Ni electroplating as an industrial method for improving the spot weldability of MSSs.
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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) | 0.000 | 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".