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The concept of using additive technologies for digital prototyping of assembly devices

2022· article· en· W4311899353 on OpenAlexaff
Andrey Vlasov, Ludmila V. Juravleva, Karim Ismagilov

Bibliographic record

VenueJournal of Physics Conference Series · 2022
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsRapid prototypingElectronicsManufacturing engineeringVirtual prototypingComputer scienceProcess (computing)Systems engineeringDesign for assemblyProduction (economics)EngineeringSimulationMechanical engineeringDesign for manufacturability

Abstract

fetched live from OpenAlex

Abstract The article discusses the main provisions of the concept of using additive technologies for digital prototyping of assembly devices in the production of electronic equipment. Approaches to the system design of devices and their components are formulated. General ideas about devices are generalized and systematized, types of fixtures for assembling electronic equipment are classified. A comparative analysis of the methods of classical and model-oriented (drawing-free) design is carried out. Recommendations on the use of additive technologies in the implementation of ‘trial and error’ design methods, the coordinate calculation method, wave technology, design using layout schemes and virtual assembly are given. The tasks that are solved in the process of designing and manufacturing devices in the production of electronic equipment are determined. Recommendations for modeling, simulation and prototyping of devices for testing designs and validating technological processes in order to reduce the time of technological preparation and production are given.

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 score0.941
Threshold uncertainty score0.343

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 head0.026
GPT teacher head0.247
Teacher spread0.221 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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