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Record W2766591297 · doi:10.1515/amm-2017-0269

Impact of Cutting Forces and Chip Microstructure in High Speed Machining of Carbon Fiber – Epoxy Composite Tube

2017· article· en· W2766591297 on OpenAlexaff
Y. Allwin Roy, K. Gobivel, K.S. Vijay Sekar, S. Suresh Kumar

Bibliographic record

VenueArchives of Metallurgy and Materials · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsImpact
FundersAll India Council for Technical Education
KeywordsMachinabilityMachiningMicrostructureMaterials scienceThrustComposite materialChip formationComposite numberDrillingFiberCarbon fiber reinforced polymerTool wearMetallurgyMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Carbon fiber reinforced polymeric (CFRP) composite materials are widely used in aerospace, automobile and biomedical industries due to their high strength to weight ratio, corrosion resistance and durability. High speed machining (HSM) of CFRP material is needed to study the impact of cutting parameters on cutting forces and chip microstructure which offer vital inputs to the machinability and deformation characteristics of the material. In this work, the orthogonal machining of CFRP was conducted by varying the cutting parameters such as cutting speed and feed rate at high cutting speed/feed rate ranges up to 346 m/min/ 0.446 mm/rev. The impact of the cutting parameters on cutting forces (principal cutting, feed and thrust forces) and chip microstructure were analyzed. A significant impact on thrust forces and chip segmentation pattern was seen at higher feed rates and low cutting speeds.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.386

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 head0.006
GPT teacher head0.243
Teacher spread0.237 · 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 designBench or experimental
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

Citations12
Published2017
Admission routes1
Has abstractyes

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