Model identification of a twin screw extruder for thermoplastic vulcanizate (TPV) applications
Bibliographic record
Abstract
Abstract Multi‐input multi‐output (MIMO) models of a twin‐screw co‐rotating extruder for thermoplastic vulcanizate (TPV) are developed using the process identification techniques. The process inputs are screw speed (SS) and barrel temperature (WT). The three outputs are motor load (ML), melt temperature (MT), and melt pressure (MP). Two appropriate rubbers for TPV applications with different physical and mechanical properties are used for the experimentation. The process model is obtained from the experimental input–output data using various identification techniques such as least squares and prediction error. Recursive online model identification is performed on the process to update the model parameters in real time. To perform the identification studies, the process data was transferred via OPC server from the local PLC (Programmable Logic Controller) to the Advanced Control and Identification toolbox in MATLAB software. The effect of rubber properties and two curative agents (Peroxide and Phenolic) in the TPV experiment are studied on the final identified models. This comprehensive model identification study provided sufficient accurate models for further model based process analysis and control for TPV applications. POLYM. ENG. SCI., 2010. © 2009 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".