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Record W4285006170 · doi:10.5006/4033

Effect of the Tempering Process on the Corrosion Performance of Wire Arc Additively Manufactured 420 Martensitic Stainless Steel

2022· article· en· W4285006170 on OpenAlexaff
Jonas Lunde, Salar Salahi, Alireza Vahedi Nemani, Mahya Ghaffari, Ali Nasiri

Bibliographic record

VenueCORROSION · 2022
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsDalhousie UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsTemperingMaterials scienceMetallurgyCorrosionMartensiteAusteniteCarbideMicrostructureFerrite (magnet)AlloyComposite material

Abstract

fetched live from OpenAlex

With the aim of modifying the microstructure and improving the corrosion performance of a wire arc additive manufactured 420 martensitic stainless steel, heat treatment cycles consisting of austenitizing at 1,150°C followed by air cooling and subsequent tempering at different temperatures (300°C, 400°C, 500°C, and 600°C) were applied to the as-printed alloy. Microstructural analysis revealed that the austenitization and subsequent air-cooling treatment led to the removal of retained austenite and delta ferrite from the as-printed structure, while the tempering process resulted in the precipitation of a variety of carbide particles at different tempering temperatures. Electrochemical tests performed in an aerated 3.5 wt% NaCl solution showed that tempering at 400°C led to the highest corrosion resistance, while tempering at 500°C deteriorated the alloy’s resistance against localized corrosion. The most stable passive layer was found to form on the 400°C tempered sample due to the uniformity of Cr-concentration in the formed carbide precipitates and their surrounding matrix. However, Cr-rich carbide precipitates formed in the 500°C tempered sample were found to deteriorate the passive film stability throughout the immersion time in the electrolyte.

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 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)0.000
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 score0.150
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.198
Teacher spread0.191 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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