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Record W4412200644 · doi:10.3390/mi16070805

Integrated CAD/CAM Approach for Parametric Design and High Precision Fabrication of Planar Curvilinear Structures

2025· article· en· W4412200644 on OpenAlexafffund
Jonas T. Churchill-Baird, O. Remus Tutunea‐Fatan, Evgueni V. Bordatchev

Bibliographic record

VenueMicromachines · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsNational Research Council CanadaWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCurvilinear coordinatesFabricationCADEngineering drawingMechanical engineeringPlanarDiamond turningComputer scienceGroove (engineering)EngineeringProcess (computing)Design flowGeometryMachiningComputer graphics (images)MathematicsElectronic engineering

Abstract

fetched live from OpenAlex

Curvilinear V-grooves are increasingly employed in functional surfaces with applications ranging from fluidics to tribology and optics. Despite their widespread use, the accurate and repeatable fabrication of curvilinear V-grooves remains challenging due to their inherent geometric complexity and the lack of relevant commercial CAD/CAM systems. To address this, the present study proposes a CAD/CAM integrated framework capable of automating the design and fabrication of functional surfaces comprising curvilinear V-grooves generated by multi-axis single-point diamond cutting (SPDC). The framework is organized into three main functional blocks supported by seven secondary modules that encompass the entire process from V-groove geometry definition to cutting. The developed framework was practically validated by fabricating sinusoidal V-grooves on a flat surface and testing the capillary flow functionality of a curvilinear pattern. These results demonstrate the relevance of the integrated framework to curvilinear V-groove fabrication, thereby offering a versatile solution for certain types of surface engineering applications.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.274
Threshold uncertainty score0.542

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 head0.013
GPT teacher head0.260
Teacher spread0.246 · 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
GenreMethods

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

Citations1
Published2025
Admission routes2
Has abstractyes

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