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Improved life of circular saws used in primary wood processing

2023· article· en· W4318703265 on OpenAlexafffund
Maryam Torkghashghaei, William A. Shaffer, Bruna Ugulino, Rémi Georges, Roger E. Hernández, Carl Blais

Bibliographic record

VenueBioResources · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsFPInnovationsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovations
KeywordsCircular sawMaterials scienceCrackingComposite materialMachiningWaterfallCrown (dentistry)RakeStructural engineeringEngineeringMetallurgyMechanical engineering

Abstract

fetched live from OpenAlex

The effects of the Engineered Micro-Geometry (EMG) of the carbide teeth of circular saws on their wear rate and resulting sawing variation for 2-time intervals were studied. The objective was to improve the wear resistance of circular saws used during the primary transformation of wood. The tests were carried out under industrial production conditions with two series of circular saws; 1- with up-sharp tips, and 2- with cutting edges honed to adopt a waterfall geometry. The duration of the tests was 255 min and 645 min. Wood studs were sampled to measure sawing variation. Recession on the rake and clearance faces of the tips as well as the width of the wear land were measured. The wear mechanisms of the cutting edges of both types of saws were studied. Chipping and cracking were the two dominant wear mechanisms observed on the up-sharp tips. Saws with waterfall hone tips showed remarkably reduced chipping and cracking. Wear rate of the latter was notably lower than that of saws with up-sharp tips at both periods of sawing. Between-stud, within-stud, and total sawing variations decreased when saws with modified cutting edges were used.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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