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Record W1967040819 · doi:10.1115/imece2014-36767

Experimental Investigation to Study Cutting Temperature During Milling of Unidirectional Carbon Fiber Reinforced Plastic

2014· article· en· W1967040819 on OpenAlexaff
Seyedbehzad Ghafarizadeh, Jean-François Châtelain, Gilbert Lebrun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversité du Québec à Trois-RivièresÉcole de Technologie Supérieure
Fundersnot available
KeywordsMaterials scienceMachinabilityComposite materialMachiningSurface roughnessFiberThermalPerpendicularSurface finishTool wearAnisotropyMetallurgyOptics

Abstract

fetched live from OpenAlex

The surface machining of Carbon Fiber Reinforced Plastics (CFRP) materials is a challenging process, given the heterogeneity and anisotropic nature of these composites, which, combined with the abrasiveness of the fibers involved, can produce some surface damage and extensive tool wear. The cutting temperature is one of the most important factors associated with the tool wear rate and machinability of these materials, which are also affected by the mechanical and thermal properties of the work material and the cutting conditions. In this work, the cutting temperature, forces and surface roughness were measured under different cutting conditions during the ball-end milling of unidirectional CFRP. Cutting speeds ranging from 200 to 350 m/min, a feed rate of 0.063 mm/rev, fiber orientation of (the angle between carbon fibers and feed direction) 0, 45, 90 and 135 degrees, and a 0.5 mm depth of cut were used. The results show that the cutting speed and fiber orientation have a significant influence on the cutting temperature and cutting force. The maximum and minimum cutting forces and temperature were achieved for fiber orientations of 90 and 0 degrees, respectively.

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: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.414

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 head0.211
Teacher spread0.206 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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