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Adaptive Toolpath Generation for Wire Geometric Elements in Cyber-physical System for Cobotic Finishing

2024· article· en· W4403677776 on OpenAlexaff
Ramy Meziane, Julien M Audet, S.H.H. Zargarbashi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsCyber-physical systemComputer scienceMechanical engineeringEngineering drawingEngineeringOperating system

Abstract

fetched live from OpenAlex

Conventional machine tools, such as CNC machines, were mainly used for finishing applications, however they lack flexibility. Over the past decades, industries have increasingly turned to robotic solutions as a cost-effective alternative capable of processing larger and more complex workpieces. This paper focuses on the development of an adaptative toolpath generation algorithm needed in a new collaborative robotic system for finishing operations. The algorithm transforms geometric elements into toolpath poses as a function of user-defined parameters such as tolerance, maximum distance between consecutive points and collision control surface offset. Implemented in C++, the algorithm is integrated into a cyber-physical system developed using Robot Operating System (ROS) with a user-friendly interface. Experiments on the platform demonstrates the algorithm’s performance.

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: none
Teacher disagreement score0.933
Threshold uncertainty score0.364

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 head0.024
GPT teacher head0.240
Teacher spread0.216 · 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
Published2024
Admission routes1
Has abstractyes

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