Passive acoustic emissions from particulates in a V-blender
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.
Bibliographic record
Abstract
CONTEXT: Regulatory agencies are recommending the development of process analytical technologies (PAT) to improve the efficiency and product quality during pharmaceutical manufacturing. OBJECTIVE: The objective of the research was to investigate the potential application of passive acoustic emission monitoring of a V-blender. MATERIALS AND METHODS: Trials were conducted with sugar spheres, lactose or MCC in a V-blender. Vibrations from acoustic emissions were measured using PCB Piezotronics accelerometers with ICP signal conditioners. RESULTS AND DISCUSSION: A wavelet filter was applied to the measured acoustic emissions to remove vibrations from the tumbling motion of the V-shell, allowing a focus on information about particle motion and interactions within the V-shell. The ideal sensor location was determined to be the lid of one of the V-shell arms due to the impact of the tumbling particles on the lid and transmission of the vibrations from other particle motion within the V-shell. The amplitude of vibrations increased with particle size due to larger particle momentum before a collision. The fill level and the V-shell scale also influenced the measured vibrations as particle motion was affected which in turn affected momentum. Changes in particle flowability could be detected through variations in the measured acoustic emissions. CONCLUSION: The measured vibrations from passive acoustic emissions reflected particle motion and interactions within a V-blender demonstrating potential as a monitoring method.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it