Effect of shrinkage and process parameters on the monitoring of bulk and surface stream temperatures in injection molding via the infrared waveguide method
Bibliographic record
Abstract
Abstract This paper analyzes for the first time the application of the infrared hollow waveguide method for the remote sensing of the temperature decay of polymer streams during injection molding. The key feature of the infrared procedure employed is its low transmission loss of the thermal energy in the mid and far infrared spectral regions. This particular advantage allows the hollow waveguide device to measure not only the bulk temperature within the polymer, as commercially available full‐core optical fiber instruments do, but also the temperature at the polymer surface. Moreover, the hollow waveguide device is able to measure quite low temperatures, which conventional thermometers cannot do either. Experimental trials have been run on a Husky injection molding press in order to investigate the effect of some process parameters and shrinkage on the bulk and surface temperature decay signals. The results showed a drastic deviation between the kinetic behaviors of the surface and bulk temperature traces throughout the injection cycle. Particularly, it was noticed that the cooling rate of the surface temperature was more affected by part shrinkage than the cooling rate of the bulk temperature. The experimental results showed also that temperatures below 60°C could be reliably measured with reasonable signal‐over‐noise ratio. Polym. Eng. Sci. 44:955–964, 2004.
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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 | 0.001 |
| 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 | 0.000 |
| 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 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".