Advances in infrared technology for the online monitoring of injection moulding: application to the understanding of the nature of contact at the polymer—mould interface
Bibliographic record
Abstract
We describe a novel online infrared method for remote sensing of the surface and the bulk temperatures of polymers during injection moulding. The method may also be applied to other polymer forming processes such as extrusion and blow moulding. The key feature of the new method is the use of a hollow optical fibre that is incorporated into the injection mould to transmit the thermal radiation from the target to the sensor. The main characteristic of the hollow optical fibre is that it exhibits low transmission loss of the thermal energy in the mid-and far-infrared, and no end reflection. This allows measurement of quite low temperatures, as low as near room temperature. Conventional optical fibre thermometers can neither measure such low temperature ranges nor measure the polymer surface temperature. In this article, we present the first online results of critical tests of the new device. A Husky injection moulding press was used for the experiments. Good correlation was found between the radiometric results and those obtained with a thermal probe inserted near the polymer—mould interface, and with infrared imaging after the polymer part was ejected from the injection mould. In the second part of the paper, we show how the new infrared device can be used to give a better insight on the time evolution of the thermal contact between polymer and mould through the different phases of a typical injection moulding cycle. The experimental results show that thermal contact between polymer and mould is not negligible and not constant with time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".