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Record W3168799578 · doi:10.1139/cjfr-2021-0076

Effects of helix angle and feed per knife on cutting forces, noise, and power consumption produced during helical planing of sugar maple wood

2021· article· en· W3168799578 on OpenAlexaffvenue
Paul Wellenreiter, Roger E. Hernández, Claudia B. Cáceres, Carl Blais

Bibliographic record

VenueCanadian Journal of Forest Research · 2021
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMapleHelix angleHelix (gastropod)Power consumptionMaterials scienceAcousticsPower (physics)GeometryPhysicsMathematicsComposite materialGeologyBotanyBiology

Abstract

fetched live from OpenAlex

A conventional straight knife cutterhead and three helical knife cutterheads were tested for planing sugar maple wood (Acer saccharum Marsh.). Effects of helix angle and feed per knife (FK) on maximum cutting forces, sound level, and power consumption were evaluated. A 3-axis dynamometer, an array microphone, and a watt transducer were used to simultaneously record the forces, sound level, and power consumption during machining, respectively. Parallel (F P ), positive and negative normal (F NP and F NN ), lateral (F L ), resultant (F R ) forces, and sound level increased as FK increased. Helical tools produced lower F P , F NP , F NN, and F R . Parallel forces tended to decrease as helical angle increased. Differences among helical tools were not significant for normal forces. Helical tools produced higher F L at medium (2.9 mm) and high (4.7 mm) feeds per knife. F R decreased as helix angle increased. The impacts of these cutting forces on the appearance 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 8 dB(A). At low FK (1.3 mm), helical tools required slightly lower cutting power.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.343

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 head0.017
GPT teacher head0.254
Teacher spread0.238 · 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 designObservational
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

Citations7
Published2021
Admission routes2
Has abstractyes

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