Real‐time monitoring of injection molding for microfluidic devices using ultrasound
Bibliographic record
Abstract
Abstract Real‐time process monitoring of the fabrication process of microfluidic devices using a polymer injection molding machine was carried out using miniature ultrasonic probes. A thick piezoelectric lead‐zirconate‐titanate film as an ultrasonic transducer (UT) was fabricated onto one end of a 4‐mm diameter and 12‐mm long steel buffer rods using a sol gel spray technique. The center frequency and 6 dB bandwidth of this UT were 17 MHz and 14 MHz, respectively. A signal‐to‐noise ratio of more than 30 dB for ultrasonic signals reflected at the probing end was achieved. The probe can operate continuously at 200°C without ultrasonic couplant and cooling. Clear ultrasonic signals were obtained during injection molding of a 1‐mm‐thick part having test patterns on its surface. Shrinkage of the molded part and part detachment from the mold were successfully monitored. Surface imperfections of the molded parts due to a lack of the sufficient holding pressure is discussed with regard to the ultrasonic velocity obtained. The presented ultrasonic probes and technique enable on‐line quality control of the molded part by optimizing the holding pressure and improvement of process efficiency by reducing the cycle time. POLYM. ENG. SCI., 45:606–612, 2005. © 2005 Society of Plastics Engineers
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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 | 0.000 | 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".