A Dimensionless Characteristic Number for Process Selection and Mold Design in Composites Manufacturing: Part II—Applications
Bibliographic record
Abstract
The dimensionless “injectability number” was devised to assist composite engineers in the fabrication of continuous fiber composites by Liquid Composite Molding (LCM), i.e., by injecting a liquid polymer resin through a fibrous reinforcement contained in a mold cavity. Part I of this article introduced the injectability number as the integral of the ratio of the injection pressure to the resin viscosity over the cavity filling time and analyzed the theoretical aspects behind this new concept. For a given mold configuration and reinforcement material characteristics, the invariance of the injectability number with regard to process parameters was demonstrated, and an initial verification in unidirectional injection cases was conducted. Part II completes the analysis by evaluating the injectability number in more complex application cases, confirming its invariance properties. The investigation, which was carried out using numerical simulations of different LCM processes and injection strategies, examined the fabrication of various composite parts: a rectangular laminate, a hood for automotive applications, a reservoir box and a fuselage section for the aerospace industry. The results indicate that more efficient injection strategies lead to lower values of the injectability number, thus enabling the use of this dimensionless number as a tool to assess the difficulty to manufacture a given part by LCM as well as to guide process selection and compare different mold configurations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".