Data-Driven Advances in Manufacturing for Batch Polymer Processing Using Multivariate Nondestructive Monitoring
Bibliographic record
Abstract
Incorporating advanced manufacturing philosophies in practice relies on efficient strategies that can use new available sensor technologies to improve quality monitoring and process understanding. One new technology is nonlinear ultrasonics, which is a multivariate nondestructive method for the characterization of produced plastic parts. Two approaches are proposed to integrate captured data for in-line quality classification and online monitoring, providing a cost-effective alternative to destructive testing. Cluster identification is evaluated with a combination of principal component analysis (PCA) and a soft class analogy method to consider products with differing quality based on information contained in the multivariate ultrasonic signal. In the second approach, a state-space dynamic model using subspace identification is applied to historical process data and correlated with the ultrasonic-based quality data for quality prediction, and an online visualization tool was proposed in combination with a nonparametric evaluation. Results were validated with experimental data from a polyethylene rotational molding process.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".