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Investigation of the Integrity of aC:H Coatings on Stainless Steel Micro-Moulds during Thermal Cycling

2018· article· en· W2888983061 on OpenAlexvenueno aff
C. A. Griffiths, Andrew Rees, Gethin Llewelyn, O. V. Fonseca

Bibliographic record

VenueJournal of Coating Science and Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsnot available
FundersEuropean Regional Development Fund
KeywordsTemperature cyclingCyclingMaterials scienceMetallurgyComposite materialThermalPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Micro-injection moulding (µIM) is a key technology for scaling down larger geometry components and can include functional features at the micrometre scale and as far as the sub-micrometre length scale. Thermal cycling of amorphous hydrogenated carbon (aC:H) coated Stainless Steel (SS) has been investigated to simulate long-term micro-injection moulding (µIM) wearing and damage. Micro indentations and cracks were made into the mould and predictions of the crack behaviour were made using thermal expansion models. Validation of the results was performed with multiple heating and cooling cycles along with hardness measurements of the damage to the coating. The undamaged surfaces showed no major deformation but the cracks were shown to propagate and change in behaviour. The first two heat cycles of the testing had the most significant effect on the substrate with varying thermal expansions of materials being the main cause. The aC:H is shown to have excellent properties for mould tool applications but delamination could occur in areas susceptible to damaged and periodic surface inspection will be required preserve tool life.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.230
Teacher spread0.215 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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