Morphology and flow effect of microinjection‐molded plastic microgears
Bibliographic record
Abstract
Interest in the microinjection molding (μIM) process has grown as the demand for microparts increased for various electronics, transportation, communications, biomedical, and other applications. Recently, we have conducted an intensive research program to understand the details of this process and the material–process–property relationships for various polymers. In the present study, a microgear micropart was presented, in order to investigate the effects of processing conditions on microstructure, mechanical properties, and thermal properties; microtomed and examined using polarized light microscopy; and differential scanning calorimetry for investigation of morphology. Various microstructural features, such as morphological layer thickness and crystalline polymorphs, were observed and analyzed in light of the thermomechanical history. A skin‐core region was observed, with spherulites predominating the core region, and highly oriented lamellae appeared in the skin layer. Numerical simulation of filling phase of microgear gives us a detailed description of the thermal history and flow, in which contribute to better understanding on the origin differences in the morphologies among the layers across the part thicknesses in view of the prevailing process conditions. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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".