MétaCan
Menu
Back to cohort
Record W2889417819 · doi:10.1115/gt2018-77224

Microstructure and Thermomechanical Fatigue Behavior of Directionally Solidified Ni-Based Superalloys in OP Condition

2018· article· en· W2889417819 on OpenAlexaff
Dongyi Seo, Seong-Moon Seo, Young-Soo Yoo, Dae‐Jin Kim

Bibliographic record

VenueVolume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy · 2018
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsNational Research Council Canada
FundersKorea Institute of Materials Science
KeywordsSuperalloyMaterials scienceMicrostructureMetallurgyAlloyCarbideAtmospheric temperature rangeComposite material

Abstract

fetched live from OpenAlex

Two directionally solidified (DS) Ni-base superalloys, one with the GTD-111 composition and the other with a modified composition derived by numerical simulation, were produced using the Bridgman method. Solution and aging heat treatments were applied to the DS alloys to produce the desired microstructures. Thermomechanical fatigue (TMF) tests were conducted under fully reversed mechanical strain (R = −1) in the temperature range of 538–927°C in laboratory air. The tests were performed under the out-of-phase (OP) loading condition at the mechanical strain range of 0.8–1.5% with multiple specimens tested at each test condition to confirm the trend in fatigue lives. In general, the OP-TMF lives of the DS alloys depended on the applied mechanical strain range as well as the microstructural features of the alloys. By analyzing the fracture surface and longitudinal section of the tested specimens, the TMF lives of the DS alloys tested at mechanical strain ranges lower than 1.5% were found to be primarily affected by the coarse carbides formed in the interdendritic regions. In this study, the main damage mechanism of each alloy under the OP-TMF condition was elucidated in terms of its microstructural features, and recommendations were made to control the microstructures of the DS alloys to achieve enhanced TMF resistance.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score1.000

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 head0.224
Teacher spread0.217 · 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.

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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueVolume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and MetallurgySame topicHigh Temperature Alloys and CreepFrench-language works237,207