MétaCan
Menu
Back to cohort

Turning and Drilling Machinability of Recycled Aluminum Alloys

2016· article· en· W2522535097 on OpenAlex
Jean Brice Mandatsy Moungomo, Donatien Nganga Kouya, Victor Songméné

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

VenueKey engineering materials · 2016
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMachinabilityAluminiumMetallurgyMaterials scienceMachiningDrilling

Abstract

fetched live from OpenAlex

A great deal of effort and research has been dedicated to recycled aluminum alloys, mainly to recycling processes and to the mechanical properties of recomposed parts; however, very limited work has been oriented towards the machinability of recycled aluminum materials. Recycled and recomposed aluminum parts sometimes need machining to obtain the final usable part shape and for assembly purposes. The acceptability of using recycled materials in design and engineering applications depends not only on their mechanical properties, but also on their machinability. This paper investigates the machinability of recycled aluminum alloys based on surface finish, cutting forces and chip formation. Two recycled foundry aluminum alloys were used: one from aluminum can covers and another from aluminum chips produced during machining. The machining operations investigated included turning and drilling under dry and wet conditions. The two tested recycled aluminum alloys showed different machinability behaviors and different part quality characteristics, suggesting that it would be desirable to consider separating aluminum wastes and chips considered for recycling by origin or type prior to melting and recasting.

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 score0.015
Threshold uncertainty score0.745

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 head0.171
Teacher spread0.165 · 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