Real‐time diagnosing polymer processing in injection molding using ultrasound
Bibliographic record
Abstract
Abstract Ultrasonic diagnosing technique with a new high‐temperature ultrasonic transducer is developed to real‐time diagnose polymer processing and its morphology changes in injection molding processing. Compared with the previous researches, the new technique can provide more and accurate information. In this study, ultrasound diagnosis shows that longitudinal wave can real‐time characterize the data of the injection process and polymer morphology changes, including melt flow arrival time, the part ejection time, filling and packing stages, polymer solidification process, and the morphology changes during polymer crystallization. Shear waves can real‐time diagnose Young's and shear storage modulus, anisotropy property of polymer in injection molding. During our research, real‐time ultrasonic diagnosis shows that the storage modulus along the vertical direction is larger than that of the parallel to the melt flow direction under our setup injection conditions. Scanning electron microscopy and dynamic mechanical analysis measurements present that it is because the crystalline lamellas of HDPE are parallel arrangement and grow in a vertical to melt flow direction owing to injection shear force under a certain injection conditions. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".