Automatic Control of Hydrostatic Weighing Apparatus in NIS Up to 20kg Based on PLC and HMI Panel
Bibliographic record
Abstract
Automation converts a work process, procedure, or equipment to automatic rather than human operation or control.Measuring solid density with high accuracy is carried out using the hydrostatic weighing method, which is one of the urgent requirements for automation in National Metrology Institutes (NMIs).This paper describes the automatic system for measuring density of solid objects in the National Institute of Standards (NIS).A Programmable Logic Controller (PLC) controller and a Human Machine Interface (HMI) control automatic weighing operation.The system consists of a main cabinet, assembly elevating platform, Mass carrier, and Water bath.The system has three induction motors for automatic calibration; the first motor is used for the assembly elevating platform.The second and third motors are used for mass carriers.This paper provides a complete description of the automation system.The feasibility of the automatic system is described using simulation software.The results of motion and the automatic system enhance the ability of the system control to measure the solid density.Finally, experimental measurements are helpful for automatically measuring the solid density of standard mass ranging from 1kg up to 20kg.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".