Pengaruh Kecepatan Screw dan Feeder pada Mesin Extruder terhadap Karakteristik Hasil Bijih Plastik
Bibliographic record
Abstract
The problem of plastic waste, especially from PET bottles, continues to increase and prompts the need for innovation in processing waste into high-value products. One method used is the extrusion process, which can produce plastic ore from recycled plastic waste. This study aims to examine the effect of variations in screw speed and feeder speed in extruder machines on the characteristics of plastic ore yield. The material used was used PET plastic bottle flakes, while the cutting speed was kept constant throughout the process. Evaluation was conducted based on the ratio between screw and feeder speed to see the impact on the homogeneity, size, and shape stability of the plastic ore produced. The data combination with the range at screw speed (Ns) = 25 rpm; feeder speed (Nf) = 10 rpm appeared repeatedly and showed stable performance based on the observation test. This combination is most likely the optimal operating parameter. At a stable screw/feeder ratio of 2:1 to 2.5:1, the ore yield is more uniform and dimensional consistency is higher. The calculation results show that the ideal cutting speed is 132 rpm. The current extruder machine tool design set at only 140 rpm constant speed is too fast for this condition. Screw speed and feed rate affect the mass flow rate and melting temperature of the material. The cutting speed must be matched with the material output rate from the extruder to produce a consistent size of plastic ore. The results show that a balanced speed ratio provides a uniform size distribution and a more stable ore shape, while speed imbalance can give rise to product defects such as bubbles, rough surface, and non-uniform shape. The results of this research discussion show the importance of proper process parameter settings in extrusion systems to produce high-quality plastic ore from recycled materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".