Impacts of post-printing heat treatment on the microstructure and mechanical properties of wire-arc additively manufactured nickel aluminum bronze alloy
Bibliographic record
Abstract
Wire-arc additive manufacturing (WAAM) is an effective process for producing and repairing large-scale Nickel Aluminum Bronze (NAB) components, particularly within the marine defense and aerospace industries. This study investigates the impact of two post-printing heat treatments —direct aging and solutionizing followed by aging—on the microstructure and mechanical properties of WAAM-NAB alloy. The as-printed WAAM-NAB alloy (As-PNAB) shows a complex microstructure with large globular κ II (Fe 3 Al) and lamellar κ III (NiAl) phases. Direct aging at 675 °C for 6 h (DA-NAB) and solutionizing at 950 °C for 2 h before aging (SA-NAB) result in a more uniform κ-phases distribution and the formation of needle-like κ IV phases. DA-NAB yields smaller precipitates, while SA-NAB achieves a more homogenous microstructure with larger precipitates that remain smaller than those in As-PNAB. DA-NAB displays superior mechanical properties, with enhanced yield strength, ultimate tensile strength, and ductility compared to SA-NAB. DA-NAB was characterized by a more uniform grain structure with reduced dislocation density, contributing to its improved mechanical performance. Direct aging was identified as the most effective heat treatment for achieving a favorable balance of strength and ductility, outperforming the solutionizing and aging approach.
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 | 0.001 |
| 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) | 0.003 | 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".