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Record W4411405427 · doi:10.1016/j.cirpj.2025.06.010

Impact of surface post-treatments on corrosion resistance in heat-treated laser-powder bed fused Nickel Aluminum Bronze

2025· article· en· W4411405427 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCIRP journal of manufacturing science and technology · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMetallurgy and Material Science
Canadian institutionsDalhousie University
FundersApolloOcean Frontier InstituteDefence Research and Development CanadaDalhousie UniversityNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMetallurgyBronzeMaterials scienceNickelAluminiumCorrosion

Abstract

fetched live from OpenAlex

This study investigates the effects of surface finishing on the passive behavior and corrosion response of C63020- Nickel Aluminum Bronze (NAB) alloy produced via laser-powder bed fusion (L-PBF) additive manufacturing , followed by annealing. To evaluate these effects, annealed L-PBF NAB in its as-printed surface finish was compared to samples subjected to two different surface treatments: one ground and another ground followed by polishing. Microstructural analysis of the samples revealed primarily globular and elongated κ III phase precipitates distributed within the α-Cu matrix. Surface roughness measurements ranked the samples from highest to lowest as: as-printed NAB, ground NAB, and polished NAB. The results revealed that rougher surfaces enhanced electrochemical performance , as NAB passivity is a time-dependent process that benefits from an increased effective surface area. As a result, the ground-only samples demonstrated the highest corrosion resistance among the evaluated conditions.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.264
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it