Predicting dimensional distortions in roll forming of comingled polypropylene/glass fiber thermoplastic composites: On the effect of matrix viscoelasticity
Bibliographic record
Abstract
Thermal deformations that occur during formation of long-fiber-reinforced composites have been a continued challenge for manufacturers as the final shape of a given part can be different from the original mold shape. The ensuing dimensional distortions can be difficult to predict due to complex thermo-mechanical behaviour of composite laminates during different forming cycles. This study intends to model the fundamental mechanisms that lead to thermal deformations during forming of a thermoplastic matrix composite comprised of comingled polypropylene and E-glass fibers. While the discussion is framed around a custom-design multi-stage roll-forming process, it is also relevant to a wider range of thermoplastic composites manufacturing processes. A methodology is developed to characterize the thermal mechanical behavior of the material, optimize the manufacturing process, and predict the magnitude of resulting spring-in angle due to thermal deformations. It is found that the process control parameters can be optimized first such that the crystallization of the matrix occurs at an ideal position along the forming line. Once the process is optimized, the developed numerical model, with a thermoelastic material behaviour, can give an adequate prediction of spring-in at the end of the process. Finally, through a comparative study, it is discussed how for other manufacturing processes, such as compression molding, including a thermoviscoelastic liquid/solid material behaviour may be required to yield accurate spring-in predictions.
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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 | 0.000 |
| 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".