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Record W2757988126 · doi:10.1177/1350650117733921

Study of tribofilms generation at different cutting speeds in dry machining hardened AISI T1 and AISI D2 steel

2017· article· en· W2757988126 on OpenAlexaff
Junfeng Yuan, Julia Dosbaeva, Danielle Covelli, Jeremy Boyd, German Fox‐Rabinovich, Stephen C. Veldhuis

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMachiningMaterials scienceMetallurgyCeramicLubricationCarbideScanning electron microscopeOptical microscopeTool steelTribologyComposite material

Abstract

fetched live from OpenAlex

Hard dry machining of AISI T1 and AISI D2 steels at comparable hardness (around 59 HRC) were performed at 50 m/min, 80 m/min and 100 m/min cutting speeds separately with uncoated alumina ceramic tool inserts (Al 2 O 3 + TiC), in order to study the effect of cutting conditions on the generation of tribofilms. Comprehensive assessment of uncoated ceramic inserts in machining of T1 and D2 at different cutting speeds was made by optical microscope, scanning electron microscope and X-ray photoelectron spectroscopy. It was shown that higher cutting speeds increased the formation of thermal protective and/or lubricating tribofilms. In machining D2 steel, more intensive formation of Cr-O tribo-oxides brought a lower wear rate; while in cutting T1 steel, a higher amount of W-O tribofilms generation provided better lubrication. At higher cutting speeds, wear/friction behaviour changes were attributed to differences in two factors: first, from a traditional micro-scale standpoint, the distribution of carbide within steels and the accumulation of thermal and mechanical damages during different cutting conditions; second, from a new nano-scale viewpoint, the generation of protective/lubricating tribofilms due to change of machining conditions.

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)0.001
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: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.021
GPT teacher head0.249
Teacher spread0.228 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part J Journal of Engineering TribologySame topicAdvanced machining processes and optimizationFrench-language works237,207