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Record W2964205452 · doi:10.5539/cis.v12n3p21

Practical Nozzle Temperature Control Method for Robot FDM Printing System

2019· article· en· W2964205452 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueComputer and Information Science · 2019
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleFused deposition modelingTemperature controlComputer scienceParticle swarm optimization3D printingVoltageTemperature measurementRobotMechanical engineeringArtificial intelligenceElectrical engineeringAlgorithmEngineeringPhysics

Abstract

fetched live from OpenAlex

Fused Deposition Modeling (FDM) technology in 3D printing has beendeveloped for many years. In this paper, a robot FDM 3D printingsystem is proposed and the nozzle temperature control issue isfocused primarily. The temperature measurement adopts a data driven modeling method andthe parameters of the measurement model are trained by the particle swarm optimization algorithm. A practical temperature control method is presented in which thetemperature control of nozzle is divided into two periods. Duringthe temperature flying period, the heating voltage is givenaccording to the current temperature value and its varying trend. In thefalling time of nozzle temperature, the corresponding controlvoltage value is provided correspondingly. Based on this practical control strategy, a partdesigned with Solidworks software is printed using the robot FDM printing system which validates theeffectiveness of the practical temperature control method.

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.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.792
Threshold uncertainty score0.253

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 communication0.0000.002
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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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