Micro Replication by Injection-Compression Molding
Bibliographic record
Abstract
Abstract The replication of polymeric microstructures on a flat surface is not easily achieved by conventional injection molding, because of the required micro-dimensional control and the stringent tolerances of most applications. Moreover, the flow behaviour of melts in micro cavities remains not well understood and challenging. On the other hand, injection-compression molding is an established process for the manufacturing of optical storage media, like compact discs (CD) or digital versatile discs (DVD), with grooves and pits at the micro-scale. The difficulties arise mainly from two sources: lack of adequate filling because of premature freezing of features with very small thicknesses and excessive deformation during ejection due to high friction at the polymer-metal interface. In this work, a study is carried out to investigate the effect of process parameters on the replication of various microstructures on a flat disk using microinjection-compression molding. A commercial microinjection molding machine has been used in the study. Two optical grade polymers poly(methyl methacrylate) (PMMA) and cyclic olefin polymer (COP) were used in conjunction with various mold inserts to reproduce the embedded microstructures. The dimensions of the microstructures on the inserts and molded parts were measured with a confocal profiler. A transcription ratio was defined to assess the quality of the replication. The design of experiment (DOE) method was used to obtain correlations between the process parameters and the development of the microstructure during injection-compression molding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".