Real-time ultrasonic diagnosis of polymer degradation and filling incompleteness in micromoulding
Bibliographic record
Abstract
Injection moulding techniques have been miniaturised and refined to achieve micromoulding which aims to satisfy the need for mass production of low-cost micro- and nanoscale components. However, the microscale mould cavity features and extreme processing conditions which are inherent in the process can result in larger process variations than conventional injection moulding, with a corresponding increase in the probability of producing an unsatisfactory product. Accurate process diagnosis is required to ensure process reliability but integration of sensors onto the small and highly detailed mould units can be problematic and alternatives may need to be sought. Piezoelectric film ultrasonic transducers were integrated onto the extrusion barrel and mould insert of a micromoulding machine for real-time, non-destructive and non-intrusive process diagnosis with an ultrasonic pulse-echo technique. Polymer degradation owing to excessive heating at the extrusion barrel was successfully probed by measuring the ultrasonic velocities in the polymer at the mould insert. Filling incompleteness of the mould cavity was also sensitively detected by monitoring the ultrasonic energy variation transmitted into the part at different points along the melt flow length. The developed ultrasonic sensors and technique enable optimisation and in-process quality assurance of the moulded parts which ensures that maximum process efficiency can be achieved.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".