MétaCan
Menu
Back to cohort

Application of High-Pressure Torsion to Al-Si Alloys with and without Scandium Additions

2010· article· en· W2017002104 on OpenAlexaff
K. Venkateswarlu, V. Rajinikanth, Mani Sen, Saleh N. Alhajeri, Terence G. Langdon

Bibliographic record

VenueMaterials science forum · 2010
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsCanadian Society of Intestinal Research
FundersNational Science Foundation
KeywordsMaterials scienceScandiumAlloyTorsion (gastropod)Grain sizeIndentation hardnessMetallurgyPrecipitationMicrostructure

Abstract

fetched live from OpenAlex

Al-2 wt. % Si alloys with and without 0.25 wt. % scandium additions were processed by high-pressure torsion up to five turns at room temperature under a pressure of 6.0 GPa. Microstructural examination of the as-cast Al-2Si-0.25Sc alloy revealed the presence of Al3Sc precipitates which refined the Al grain structure, whereas no major changes were observed in the morphology of the Si particles. Processing by HPT of both experimental alloys revealed submicrometer grains with uniformly distributed Si particles. The mechanical properties were obtained using hardness measurements and the ball-indentation technique. The results show the hardness increased in the first turn of HPT and further increased with increasing numbers of turns. In addition, the hardness values were lower at the centers and continuously increased towards the edges of the disks. The difference in hardness values between the centre and the edge decreased with increasing turns, thereby suggesting an increasing homogeneity with increasing processing. The scandium addition and HPT processing of the Al-2Si alloy strongly influences the grain refinement and mechanical properties. The grain size reduction in the Al-2Si alloy was similar to Al whereas the presence of Sc in Al-2Si during HPT processing was responsible for large precipitation networks and a submicrometer grain formation.

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)0.001
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 score0.007
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.225
Teacher spread0.220 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueMaterials science forumSame topicMicrostructure and mechanical propertiesFrench-language works237,207