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Record W4402660676 · doi:10.1016/j.jmrt.2024.09.119

Effect of heat treatment before fast multiple rotation rolling on friction surfaced Al–Si–Cu alloy

2024· article· en· W4402660676 on OpenAlexaff
Seyedeh Marjan Bararpour, Hamed Jamshidi Aval, Roohollah Jamaati, Mousa Javidani

Bibliographic record

VenueJournal of Materials Research and Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMaterials scienceAlloyRotation (mathematics)MetallurgyComputer science

Abstract

fetched live from OpenAlex

In this study the effect of combination of heat treatment of consumable rod and solid solution heat treatment before fast multiple rotation rolling (FMRR) processing on microstructure, mechanical property and wear resistance of Al–Si–Cu alloy friction surfaced on commercial pure aluminum alloy were investigated. Results show that after the FMRR process, there is a significant reduction (99% reduction) in the friction surfaced coating surface roughness. The surface roughness after FMRR processing in the coatings created by homogenized and solid solution treated consumable rod is 0.74 ± 0.12, and 0.56 ± 0.08 μm, respectively. In the coating created by homogenized rod the minimum grain size (1.7 ± 0.2 μm) formed in the FMRR processed layer. Using homogenized consumable rod and FMRR processing by rotational speed of 3000 rpm and traverse speed of 140 mm/min, the maximum hardness (9.8 ± 0.3 GPa) and minimum wear rate (4.6 ± 0.1 μg/m) created at processed layer. FMRR processing by rotational speed of 3000 rpm and traverse speed of 140 mm/min, result in 27 and 24 % increasing in hardness at friction surfaced coating created by homogenized and solid solution treated consumable rods, respectively.

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 score0.010
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.286
Teacher spread0.272 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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