Three‐dimensional modelling and simulation of sequential co‐injection moulding with application to a toothbrush handle
Bibliographic record
Abstract
Sequential co‐injection moulding (SCIM) is a promising technique in industrial production. Because of the complex hydrodynamic effect of air on polymer melts in a moulding process, it is difficult to accurately design and control the process. Corresponding theoretical investigations are very limited, especially for the transient free surface flows in the filling stage. In this paper, a three‐dimensional (3‐D) unified model is proposed for simulating fluids flow in SCIM. The melted polymer and air in the cavity can be regarded as a continuous fluid. The evolution of the melt front interface and skin/core melt interface are captured simultaneously at any moment by the level set method; this method has been widely‐used in two‐phase flow for its ability to track interfaces. The finite volume method is combined with a domain extension technique to deal with 3‐D flows in an irregular cavity. The model is validated by simulating the SCIM process for a cavity with a block insert, and a distinct corner effect is observed. For a widely‐used plastic toothbrush made by SCIM, the evolutions of transient free surfaces in the filling stage are investigated. All numerical results are consistent with corresponding experimental results, and demonstrate the capability of the model with the domain extension technique to simulate multiphase flows in SCIM.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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".