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Record W2895946939 · doi:10.2351/1.5062981

3D finite element modelling of laser-assisted maskless micro-deposition

2013· article· en· W2895946939 on OpenAlexaff
Amir Azhari, Elahe Jabari, Ehsan Toyserkani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceFinite element methodConstitutive equationDeposition (geology)LaserThermal conductionSelective laser sinteringCladding (metalworking)Composite materialForming processesStress (linguistics)OpticsSinteringStructural engineeringPhysics

Abstract

fetched live from OpenAlex

The manuscript addresses a 3D finite element modelling of a laser-assisted maskless micro-deposition process (aka micro laser sintering/cladding) where it is utilized in the production of micro-tracks composed of silver nano-particles. The model is established through a coupled heat conduction equation with strain/stress constitutive physics in the presence of a moving continuous or pulsed laser. The appropriate number of mesh elements is adopted for micro-scale domains to obtain error-free numerical results. The model predicts temperature variations along with the formation of stress, strain, and subsequently, structural defects like cracks throughout the substrate. The concentration of stress due to the geometry and also, thermal properties is studied to predict the critical points of failure in the structure.

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: none
Teacher disagreement score0.508
Threshold uncertainty score0.960

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)0.0010.000

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 head0.018
GPT teacher head0.207
Teacher spread0.188 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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