Application of ultrasound and neural networks in the determination of filler dispersion during polymer extrusion processes
Bibliographic record
Abstract
Abstract Mineral filler dispersion is important information for the production of mineral‐charged polymers. In order to achieve timely control of product quality, a technique capable of providing real‐time information on filler dispersion is highly desirable. In this work, ultrasound, temperature, and pressure sensors as well as an amperemeter of the extruder motor drive were used to monitor the extrusion of mineral‐filled polymers under various experimental conditions in terms of filler type, filler concentration, feeding rate, screw rotation speed, and barrel temperature. Then, neural network relationships were established among the filler dispersion index and three categories of variables, namely, control variables of the extruder, extruder‐dependent measured variables, and extruder‐independent measured variables (based on ultrasonic measurement). Of the three categories of variables, the process control variables and extruder‐independent ultrasonically measured variables performed best in inferring the dispersion index through a neural network model. While the neural network model based on control variables could help determine the optimal experimental conditions to achieve a dispersion index, the extruder‐independent network model based on ultrasonic measurement is suitable for in‐line measurement of the quality of dispersion. This study has demonstrated the feasibility of using ultrasound and neural networks for in‐line monitoring of dispersion during extrusion processes of mineral‐charged polymers. POLYM. ENG. SCI., 45:764–772, 2005. © 2005 Society of Plastics Engineers
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 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".