MétaCan
Menu
Back to cohort
Record W4380083146 · doi:10.1504/ijat.2023.10056894

Investigation on Workpiece Microstructure and Wheel Performance on Grinding Titanium Metal Matrix Composites

2023· article· en· W4380083146 on OpenAlexaff
Luc Baron, Marek Balazinski, Zhongde Shi, Cécile Escaich

Bibliographic record

VenueInternational Journal of Abrasive Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Machining and Optimization Techniques
Canadian institutionsNational Research Council CanadaPolytechnique Montréal
Fundersnot available
KeywordsMaterials scienceMicrostructureComposite materialGrindingMatrix (chemical analysis)MetalTitaniumMetal matrix compositeGrinding wheelMetallurgy

Abstract

fetched live from OpenAlex

An experimental study is reported on the grinding of a titanium metal matrix composite (TiMMC) using electroplated diamond wheels. Flat surface grinding experiments were performed at a fixed wheel speed vs = 20 m/s, various depths of cut a = 0.03-0.08 mm, and workspeeds vw = 10-100 mm/s. Additional tests were also conducted on titanium alloy (Ti alloy) for comparisons. The wheel wear and its effects on grinding forces, power, surface roughness, and workpiece microstructures are presented. The process limits were also investigated. It was revealed that steady state wheel wear on TiMMC was slightly slower than on Ti alloy. Low specific energy ranging from 16-34 J/mm3 was obtained for both materials. It was found that surface roughness decreases for TiMMC, but increases for Ti alloy, with increasing workspeeds. Plastic deformation leads to microstructural changes in the form of twinning below ground surfaces.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Abrasive TechnologySame topicAdvanced Machining and Optimization TechniquesFrench-language works237,207