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Record W3173875301 · doi:10.18280/jesa.540309

Remarkable Characteristics of a Novel Path Interval Determination in Filleted End Milling

2021· article· en· W3173875301 on OpenAlexvenueno aff
Tsutomu Sekine, Kyoko Kameya

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsnot available
FundersSeikei University
KeywordsInterval (graph theory)Path (computing)Numerical controlIntersection (aeronautics)Boundary (topology)Tool pathProcess (computing)Computer scienceEnd millingMechanical engineeringEngineering drawingEngineeringMathematicsMachiningMathematical analysisAerospace engineering

Abstract

fetched live from OpenAlex

This study describes remarkable characteristics of a novel path interval determination in filleted end milling with a tool inclination. CNC milling machine is one of the core technologies in practical manufacturing. Computer-aided technologies have contributed to the technological advancement. Tool path generation in computer-aided manufacturing is really important for CNC milling process. Although there are a lot of parameters treated in tool path generation, path interval is one of the influential factors in considering the balance between manufacturing efficiency and machined surface feature. A path interval determination in filleted end milling commonly entails the intersection problems with mathematical complexities in essence. The related studies have been reported so far, while there has scarcely been a procedure to cope effectively with the complexities. Hence, this study focused on a novel path interval determination proposed in our previous study. After the analytical discussions were made with the computational and experimental results, it was acquired from the explicit evidences that the novel procedure possessed remarkable characteristics contributable for a path interval determination in multi-axis filleted end milling.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.660
Threshold uncertainty score0.592

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 head0.015
GPT teacher head0.243
Teacher spread0.228 · 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
Published2021
Admission routes1
Has abstractyes

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Same venueJournal Européen des Systèmes AutomatisésSame topicAdvanced machining processes and optimizationFrench-language works237,207