MétaCan
Menu
Back to cohort

Advanced nanomechanical testing for high speed machining of hard-to-cut aerospace alloys

2011· article· en· W2076933100 on OpenAlexaff
Ben D. Beake, German Fox‐Rabinovich

Bibliographic record

VenueInternational Heat Treatment and Surface Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMachiningMaterials scienceNanoindentationAerospaceNanomanufacturingIndentationCoatingMechanical engineeringNanotechnologyComposite materialMetallurgyAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Designing coatings for increasing the lifetime of cutting tools in high speed machining of the new generation of aerospace alloys is a considerable challenge. During high speed cutting, significant heat is generated by friction in the cutting zone, and for interrupted contact (e.g. end or face milling), fatigue and fracture resistance is also important. Improvements to nanomechanical instrumentation have enabled nanoscale measurements to be reliably made under conditions that closely mimic those in real contacts. Nanomechanical data at elevated temperatures either quasi-statically (nanoindentation) or repetitively at high strain rates (nano-impact/impulse indentation) are used to improve coating performance and provide greater understanding of key factors controlling wear resistance under different contact conditions. The data show excellent correlation with field trials of coated cutting tools in high speed machining. It is expected that advanced nanomechanical test methods will increasingly speed up the pace of materials development for extreme 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.768

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 head0.035
GPT teacher head0.224
Teacher spread0.189 · 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

Citations10
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueInternational Heat Treatment and Surface EngineeringSame topicMetal and Thin Film MechanicsFrench-language works237,207