Injection molding shrinkage of PP: Experimental progress
Bibliographic record
Abstract
Abstract Injection molding shrinkage deals with dimensional differences between a molded part and the cavity. By adding an array of orthogonal marks into a mold, local shrinkage values may be obtained by comparing dimensions of this array with dimensions of the array replicated on the surface of the parts. A profilograph is employed to obtain dimensional measurements, in the parallel to flow direction and in the cross flow direction. A sensitivity analysis is conducted to determine aspects of shrinkage evaluation causing uncertainty on the results. Prominent sources of uncertainty found are mark straightness defect and part warpage. Uncertainty on shrinkage is evaluated to 0.00025 mm/mm for a distance between the marks of 6.350 mm. Shrinkages have been evaluated locally for molded plates. Different distribution forms were observed for parallel to flow and cross flow shrinkage. Important anisotropy is also observed. The effects of holding pressure and injection velocity on shrinkages have been evaluated using a 23 factorial design of experiment for three locations on the plates. Finally, shrinkages for three mold geometries have been compared: constant thickness plate, variable in thickness symmetrical plate, and variable in thickness asymmetrical plate. Variable in thickness plates showed the importance of solidification dynamics on final shrinkages. POLYM. ENG. SCI., 46:1275–1283, 2006. © 2006 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".