Experimental investigation into the performance of cutting betel nut machine via response surface methodology and desirability function
Bibliographic record
Abstract
Cutting betel nut machines are increasingly being designed by engineers using local material. However, the performance of the cutting betel nut machine is influenced by the moisture content of the betel nut and the rotational speed of the machine. In this study, the performance of cutting a betel nut machine under moisture content of betel nut and rotational speed of the machine was studied using response surface methodology (RSM) and desirability function. Central Composite Design (CCD) coupled with RSM and desirability function was employed to evaluate the impact of moisture content of betel nut (34.68–50.54%, w.b.) and rotational speed (600–1000 rpm) on machine capacity (kg/hr), efficiency (%), and losses (%) responses. The desirability function was then used to optimize moisture content and rotational speed yielding maximum machine capacity and efficiency at lower losses. Three verification experiments were run to ensure the empirical relationships were valid. Optimum requirements of process parameters have been seen at which moisture content of 50.54% (w.b.) and rotational speed of 1000 rpm was achieved in maximum machine capacity of 44.16 kg/hr at higher efficiency (92.72%) and lower losses (6.31%). The model's conclusions were very consistent with the confirmed values. The results proved that an appropriate performance of the machine can be achieved using moisture content of betel nut and rotational speed of machine cutting betel nut.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".