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Record W4297158756 · doi:10.3390/ma15196606

Effect of Fe and C Contents on the Microstructure and High-Temperature Mechanical Properties of IN625 Alloy Processed by Laser Powder Bed Fusion

2022· article· en· W4297158756 on OpenAlexaff
Alena Kreitcberg, Vladimir Braïlovski

Bibliographic record

VenueMaterials · 2022
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMaterials scienceMicrostructureAlloySuperalloyAnnealing (glass)CarbideMetallurgyDuctility (Earth science)InconelFusionCreep

Abstract

fetched live from OpenAlex

Two alloys with different Fe and C contents were studied to assess the influence of their compositions on the microstructure and mechanical properties of Ni-based Inconel 625 superalloy processed by laser powder bed fusion and subjected to stress relief annealing (870 °C) and a solution treatment (1120 °C). It was concluded that the alloy with a higher Fe content (~4 wt.% as compared to ~1 wt.%) manifests a greater propensity to segregate Nb and Mo elements during printing and form δ phase particles during the stress relief annealing. On the other hand, the alloy with a higher C content (~0.04 wt.% compared to ~0.02 wt.%) exhibits a greater tendency to form M6C carbides during the solution treatment. No effects of the Fe and C content variations on the room temperature mechanical properties were observed. On the contrary, an increase in the C content resulted in a 40% lower high-temperature (760 °C) ductility of the laser powder bed fused and post-processed IN625 alloy, without affecting its strength characteristics.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.179
Teacher spread0.174 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2022
Admission routes1
Has abstractyes

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