MétaCan
Menu
Back to cohort
Record W2691146574 · doi:10.23977/jemm.2016.11003

Research on Dynamic Characteristic on the Circular Saw Blade of the Plastic Doors and Windows Corner Cleaning Based on ANSYS

2016· article· en· W2691146574 on OpenAlexvenueno aff
LI Guo-ping, Zhenyong Huang

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2016
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsCircular sawDoorsVibrationStructural engineeringBlade (archaeology)EngineeringNatural frequencyMechanical engineeringAcousticsPhysics

Abstract

fetched live from OpenAlex

In the plastic profiles corner cleaning process, the axial vibration of the end mill caused the corner cleaning quality is poor. So, it used the SolidWorks to establish the three-dimensional entity model of the circular saw blades in this article, and then the natural frequencies and the corresponding vibration types of the circular saw blades have been obtained from model analysis of the circular saw blade of the plastic doors and windows corner cleaning machines by using ANSYS, and the analysis of transverse vibration stability of the low frequency range. It was concluded that the allowable speed range of the circular saw blade was 1000rpm-6459rpm and a larger diameter chuck was utilized to enhance the vibration stability of the circular saw blade. The research aims to provide reference data for rationalization of the circular saw blade parameters selection and enterprise actual plastic doors and windows corner cleaning. It has an important theoretical value to improve the quality of the plastic doors and windows corner cleaning.

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)0.001
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 score0.226
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.012
GPT teacher head0.220
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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Engineering Mechanics and MachinerySame topicTunneling and Rock MechanicsFrench-language works237,207