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Record W4415551767 · doi:10.5614/joki.2025.17.2.1

Prototype of 100 ml Measuring Cylinder Pouring Tool using Microcontroller-Based Servo Motor

2025· article· W4415551767 on OpenAlexaff
Azis Muslim, Regita Robi'ah, Herfin Yienda Prihensa

Bibliographic record

VenueJurnal Otomasi Kontrol dan Instrumentasi · 2025
Typearticle
Language
FieldEngineering
TopicIndustrial Automation and Control Systems
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsCylinderServoServomotorTilt (camera)Stepper motorProcess (computing)ActuatorPneumatic cylinder

Abstract

fetched live from OpenAlex

Pouring liquid from a measuring cylinder requires careful consideration of the appropriate tilt angle to achieve optimal emptying conditions. Drip time and emptying time are critical parameters for maximizing pouring efficiency. This study presents the design of a microcontroller-based system to automate the pouring process of a measuring cylinder. The developed prototype utilizes an MG996R servo motor as the actuator and an Arduino Uno microcontroller as the control unit. The system comprises two main components: a servo motor-based mechanical pouring structure and microcontroller-based automation software. The initial phase involved designing the structure using 3D modelling software, manufacturing, and manual assembly. Simulations were conducted using SolidWorks software. Experimental results showed that the liquid pouring time at a 120° angle was 9.89 seconds with a 4.76% error, at 150° was 12.38 seconds with a 3.78% error, and at 170° was 13.97 seconds with a 2.73% error. The total emptying time for a 100 ml measuring cylinder was recorded at 62.69 seconds with a 0.81% error.

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)0.001
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.023
GPT teacher head0.244
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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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