Effect of heat treatment on microstructural evolution and corrosion behavior of wire-arc additive manufactured nickel aluminum bronze alloy
Bibliographic record
Abstract
In the pursuit of enhancing the corrosion resistance of wire arc additive manufactured nickel aluminum bronze (WAAM-NAB) alloy, this study explores its microstructural evolution and corrosion behavior before and after heat-treatment. The findings unveil that annealing significantly improves the alloy’s microstructure by increasing homogeneity, grain size, and κ-phase distribution, while reducing residual stress and promoting low-energy Σ3–60°//[111] boundaries. These microstructural improvements enhance the alloy’s corrosion resistance in a 3.5 wt% NaCl solution by decreasing susceptibility to localized and galvanic corrosion and promoting stable passive film formation. Consequently, the heat-treated NAB-alloy shows superior corrosion performance, offering greater durability over its as-printed state. • NAB alloy was developed using the wire arc additive manufacturing technique. • Heat-treated NAB alloy demonstrated an enhanced microstructure, promoting greater homogeneity. • Corrosion behavior of both the AP-NAB and HT-NAB alloys were evaluated in a 3.5 wt% NaCl solution. • Corrosion resistance of both the AP-NAB and HT-NAB alloys exhibited improvements over time. • HT-NAB alloy displayed superior corrosion performance over its as-printed state.
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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".