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Record W7043704106

Study of chip formation mechanism in CFRP turning

2021· other· en· W7043704106 on OpenAlexaff

Bibliographic record

VenueLume (Universidade Federal do Rio Grande do Sul) · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsDalhousie University
Fundersnot available
KeywordsChip formationMachiningChipComposite numberCarbon fiber reinforced polymerFibre-reinforced plasticFiberMechanism (biology)
DOInot available

Abstract

fetched live from OpenAlex

Carbon fiber reinforced polymer (CFRP) is a composite material much less studied than steel and other metals. This material class has excellent properties and allows weight reduction in structural parts; however, the CFRP turning has not the same popularity in the industry of other machining processes as milling and drilling. In this work, a literature survey was conducted to clarify the behavior of CFRP in machining, especially turning. This study evaluated the influence of the composition of the material and the fiber orientation angle, the surface damage during chip formation, and, mainly, parameters as cutting speed, feed rate, depth of cut, and tool geometry and material in the chip formation mechanisms. After discussion, some gaps are identified to be explored using PCD and tools under high cutting parameters conditions. After considering the evaluated studies, an experimental procedure is proposed for future execution, in which the effect of the cutting speed, depth of cut, and tool clearance angle.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.018
GPT teacher head0.240
Teacher spread0.222 · 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
Published2021
Admission routes1
Has abstractyes

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