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Record W3214683459 · doi:10.1115/imece2000-1906

A Mechanistic Force Model of the 5-Axis Milling Process

2000· article· en· W3214683459 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueManufacturing engineering · 2000
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsMcMaster University Medical Centre
Fundersnot available
KeywordsCartesian coordinate systemRobustness (evolution)Tungsten carbideCalibrationComputer scienceEnhanced Data Rates for GSM EvolutionScalabilityIntersection (aeronautics)Mechanical engineeringProcess (computing)Milling cutterDiscretizationMaterials scienceEngineeringGeometryMachiningMathematicsMathematical analysisArtificial intelligenceComposite material

Abstract

fetched live from OpenAlex

Abstract This paper presents a five-axis milling force model that can incorporate a variety of cutters and workpiece materials. The mechanistic model uses a discretized cutting edge to calculate an area of intersection which is multiplied by the specific cutting pressure to produce a force output along the primary cartesian coordinate system. By using an analytic description of the cutting edge with a non-specific cutter and workpiece intersection routine, a model was created that can describe a variety of cutting situations. Furthermore, a back propagation neural network is used to calibrate the model, providing robustness and scalability to the calibration process. Testing was performed on 1020 steel using various cutting parameters with a high speed steel two flute cutter and a tungsten carbide insert cutter. Furthermore, both linear cuts and a test die surface yielded good agreement between predicted and measured results.

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.

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 score0.526
Threshold uncertainty score0.535

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 head0.005
GPT teacher head0.181
Teacher spread0.176 · 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