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Record W2895826101 · doi:10.2351/1.5061202

Diamond reinforced metal coating using automated laser fabrication

2007· article· en· W2895826101 on OpenAlexafffund
Mehrdad Iravani-Tabrizipour, C. P. Paul, Amir Khajepour, Stephen F. Corbin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsUniversity of Waterloo
FundersOntario Centres of Excellence
KeywordsFabricationMaterials scienceDiamondCoatingToughnessLaserCharacterization (materials science)MetallurgyComposite materialNanotechnologyOptics

Abstract

fetched live from OpenAlex

Automated Laser Fabrication is an emerging advanced technology which penetrating in different aspects of manufacturing. This paper reports the automation laser fabrication of low-cost diamond tools. The engineered composition of the diamond and Cu-Sn-Ti are selected to address the issues, like-dissociation of diamond particles in metal matrix, good substrate-clad bonding etc. to ensure crack-free and pore-free deposition. The initial experiments pre-placed experiments show a promising trend. The process parameters are being optimized to obtain desired mechanical properties (like-hardness, wear resistance, toughness etc.) with sound metallurgical bonding. The paper will describe the optimization of process parameters along with material characterization.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.325

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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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

Citations1
Published2007
Admission routes2
Has abstractyes

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