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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 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.

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.

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 score0.001
Threshold uncertainty score0.377

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 head0.005
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