Manufacturing of bio‐based thermoplastic composites using industrial process for high‐volume applications
Bibliographic record
Abstract
Abstract The use of high strength‐to‐weight ratio materials in automotive component is a solution to reduce fuel consumption and decrease greenhouse gases emissions. Fiber reinforced composite materials are believed to enable weight saving that cannot be achieved with metals while meeting component mechanical requirements. Moreover, the use of renewable bio‐based composites is seen as a key aspect to further address the reduction of greenhouse gases emission. However, composite manufacturing process must meet short cycle time, performance, and cost targeted by the transportation industry in order to be considered as replacement materials. This article investigates the use of an industrial compression—direct long fiber thermoplastic (D‐LFT) process line to manufacture cellulose reinforced bio‐based polyamide parts in order to increase the penetration of sustainable materials in the transportation sectors. Process evaluation and composite performance were first carried out on a simple 2D geometry. Then the use of 100% bio‐sourced materials with the high‐throughput D‐LFT process was validated with the manufacturing of an automotive composite seat pan demonstrator. Bio‐based composite parts were successfully manufactured. Tensile properties in the range of glass fiber reinforced polypropylene composites traditionally used with the D‐LFT process were measured, validating the potential of cellulose–biopolyamide composite materials for industrial applications.
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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".