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Record W2891024552 · doi:10.1109/lawp.2018.2869580

Investigation of the 3D Printing Roughness Effect on the Performance of a Dielectric Rod Antenna

2018· article· en· W2891024552 on OpenAlexaff
Mohammad Mahdi Honari, Rashid Mirzavand, Hossein Saghlatoon, Pedram Mousavi

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceDielectricFabricationSurface roughnessAntenna (radio)OpticsOptoelectronicsComposite materialElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

In this letter, the roughness effect of a three-dimensional (3-D) printed dielectric rod antenna is investigated. One of the issues of 3-D printing in microwave components and antennas is the limited printing resolution, which creates a surface roughness on printed devices and deteriorates the performance. The effect of surface roughness on the radiation characteristics and impedance matching of a dielectric rod antenna is studied. The roughness as a perturbation brings the antenna out of its optimum design by changing the E-field intensity alongside the rod. The whole antenna consisted of a coaxial to waveguide adapter and the dielectric rod is 3-D printed and the adaptor is dipped into a low viscosity solution of silver epoxy and isopropanol to coat a conductor layer on the inner surfaces of the waveguides. The fabrication method is cost effective and much easier than conventional methods.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
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 head0.011
GPT teacher head0.201
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), 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

Citations32
Published2018
Admission routes1
Has abstractyes

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