Numerical simulation of shrinkage and warpage deformation of an intermittent-extrusion blow molded part: validation case study
Bibliographic record
Abstract
Intermittent extrusion blow molding is increasingly being used in polymer forming processes for the production of complex thermoplastic industrial parts with short cycle times. During this process residual stresses caused by inhomogeneous cooling and relaxation of polymer chains, often result in shrinkage and warpage of the final part. One challenging quality requirement of industrial blow molded parts is geometric tolerances. Therefore part deformation, due to cooling and solidification, needs to be controlled and optimized according to specific design criteria. In particular, the complex design shapes of plastic fuel tank (PFT) shells exacerbate these challenges which need to be resolved upfront, in the early stages of product development and tool design. Consequently, the development of an accurate simulation tool, well suited for industrial applications, to predict thermoplastic part deformations due to cooling and solidification, has become essential for part designers to help achieve an efficient production with minimum manufacturing cost. The aim of this work is to present the latest advancements in predicting the shrinkage and warpage deformation of a curved PFT, designed for agricultural machinery, using NRC’s BlowView software. This case study validation considers the entire blow molding stages (i.e., polymer flow in the die, parison formation, inflation, and finally in and out of mold cooling during part solidification). The simulation results, in terms of thickness distribution and displacements, are compared to an actual scanned part using the best fit technique in order to exemplify the accuracy and reliability of the modelling approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".