Process development of compression resin transfer moulding of a complex demonstrator part
Bibliographic record
Abstract
Reduction of the process cycle time has been a major challenge faced by the ground transportation industry when introducing composite components in their design. As a result, composite material manufacturers developed innovative solutions to tackle this problem. The past decade has seen resin manufacturers produce fast curing thermoset resins that can help composite manufacturers reduce process cycle times. Currently, automotive manufacturers use the liquid composite moulding (LCM) process to produce high quality net shape automotive parts, mostly for high performance low volume luxury automobiles. However, the ground transportation industry is a cost driven industry and mass production can be extremely expensive. Among all the LCM processes available, compression resin transfer moulding (CRTM) is seen as a cost-effective solution to meet industry demands. Yet, fast curing resins pose a major challenge in producing high quality parts. Therefore, researchers have been working on process simulation to optimize CRTM for the last three decades. Even though great progress has been achieved in performing CRTM simulation, there is still a huge gap to be addressed in terms of a fully coupled simulation (heat transfer, flow and mechnical) for a large complex 3D part. Hence, this paper aims at simulating the CRTM process using a new approach introduced into the current solver of PAM RTMTM. The simulations were validated using a 3D complex demonstrator part.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".