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Record W2984738609 · doi:10.1139/tcsme-2019-0215

Machinability study of aircraft series aluminium alloys 7075-T6 and 7050-T7451

2019· article· en· W2984738609 on OpenAlexaffvenue
Ali Yeganefar, Seyed Ali Niknam, Victor Songméné

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMachinabilityMachiningMaterials scienceMetallurgyAluminiumToughnessAluminium alloyLubricationCorrosionAlloyBreakageResidual stressComposite material

Abstract

fetched live from OpenAlex

The aluminium alloy 7050-T7451 is generally considered as the principal choice in aeronautical applications demanding adequate strength, stress corrosion cracking resistance, and toughness. Surprisingly, despite extensive research works on machining and machinability of aluminium alloys, including aluminium alloy 7075-T6, limited information was found on machining and machinability evaluation of 7050-T7451, which belongs to a similar family as 7075-T6. To remedy the lack of knowledge determined, dry ball-end milling operations were performed with coated end milling tools on both materials. Experimental characterization and cutting force measurements were performed to measure/evaluate the cutting forces, burr formation morphology, insert performance (wear/breakage), and surface quality attributes. According to experimental studies, 7050-T7451 was found more machinable than 7075-T6. Less burr formation and better surface quality were observed on 7075-T6. Machining attributes are influenced by different experimental factors. However, other machinability attributes, including residual stress, vibration modes, as well as particle emission, must be studied under various lubrication modes and machining operations in subsequent studies. This also recalls further studies on simultaneous multiple response optimization.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.576
Threshold uncertainty score0.489

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 head0.194
Teacher spread0.189 · 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 designSimulation or modeling
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

Citations9
Published2019
Admission routes2
Has abstractyes

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