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Record W4226221857 · doi:10.1080/17480272.2022.2056079

Cutting forces and noise in helical planing black spruce wood as affected by the helix angle and feed per knife

2022· article· en· W4226221857 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWood Material Science and Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHelix angleHelix (gastropod)Materials scienceNoise (video)Sound pressureTurn (biochemistry)Composite materialAcousticsPhysicsGeologyNuclear magnetic resonance

Abstract

fetched live from OpenAlex

A conventional straight knife cutterhead and three helical knife cutterheads were tested for planing black spruce wood (Picea mariana (Mill.) B.S.P.). Effects of helix angle and feed per knife (FK) on maximum cutting forces and sound level were evaluated. A 3-axis dynamometer and an array microphone were used to simultaneously record the forces and the sound level, respectively. Parallel (FP), lateral (FL), resultant (FR) forces, and sound level increased as FK increased. Helical tools produced lower FP, positive and negative normal forces (FNP and FNN), and FR. Parallel forces tended to decrease as helix angle increased at high FK (4.7 mm). Differences among helical tools were not significant for normal and resultant forces. Cutterheads with the two highest helix angles (50° and 60°) produced higher FL at low (1.3 mm) feed per knife. Impacts of these cutting forces on the production of surface defects and ways to reduce them were discussed. Helical cutterheads considerably generated lower sound pressure level, with a maximum difference of up to 11.5 dB(A).

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 score0.377
Threshold uncertainty score0.478

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 head0.178
Teacher spread0.173 · 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