Flow and Thermal History Effects on Morphology and Tensile Behavior of Poly(oxymethylene) Micro Injection Molded Parts
Bibliographic record
Abstract
Abstract The micro injection molding process is a rapidly growing area in plastics processing technology. In this process, the polymer is exposed to both high shear rates and large thermal gradients. In view of the versatility of the process, both commodity and engineering polymers have been used in micro injection molded products. In the present work, poly(oxymethylene) (POM), a partially crystalline engineering polymer, was employed to evaluate the relationships between processing conditions, on one hand, and the morphology and properties of the final part, on the other hand. An unsymmetrical mold cavity to make parts in the form of stepped plaques was used in the study. This resulted in substantial differences in morphology, crystallinity and shrinkage of the zones of different constant thicknesses in the micro parts. Depending on the molding conditions and the location on the micro-part, the microstructure can display up to five crystalline layers. Of particular interest, shish-kebab crystalline structures were observed within the skin of the step with the smallest thickness. Differential scanning calorimetry (DSC) tests are used to distinguish between the melting points of the shish and kebab components of this particular structure. The degree of crystallinity as determined by wide angle X-ray diffraction (WAXD) and shrinkage across the thickness were also found to be highest in the step with the smallest thickness.
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".