Residual thermal stresses in injection moldings of thermoplastics: A theoretical and experimental study
Bibliographic record
Abstract
Abstract Internal stresses in injection molded components, a principal cause of shrinkage and warpage, are predicted using a three‐dimensional numerical simulation of the residual stress development in moldings of polystyrene and high‐density polyethylene. These residual stresses are mainly frozen‐in thermal stresses due to inhomogeneous cooling, when surface layers stiffen sooner than the core region as in free quenching. Additional factors in injection molding are the effects of melt pressure history and mechanical restraints of the mold. Transient temperature and pressure fields from simulation of the injection molding cycle are used for calculating the developing normal stress distributions. Theoretical predictions are compared with measurements performed on injection molded flat plates using the layer removal method on rectangular specimens. The thermal stress development in the thinwalled moldings is analyzed using models that assume linear thermo‐elastic and linear thermo‐viscoelastic compressible behavior of the polymeric materials. Polymer crystallization effects on stresses are examined. Stresses are obtained implicitly using displacement formulations, and the governing equations are solved numerically using a finite element method. Results show that residual stress behavior can be represented reasonably well for both the amorphous and the semicrystalline polymer. Similarities in behavior between theory and experiment indicate that both material models provide satisfactory results, but the best predictions of large stresses developed at the wall surface are obtained with the thermo‐viscoelastic analysis.
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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 | 0.001 |
| 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".