Sheet‐Molded Polyolefin Natural Fiber Composites for Automotive Applications
Bibliographic record
Abstract
Abstract The use of natural fibers as reinforcing filler in thermoplastics is a relatively new application and has great potential in replacing glass fiber products in automotive industry. However, most of the research in this area has been focused primarily on flax fiber. In the first part of the work presented here, hemp fiber non‐woven mats are used exclusively in combination with a poly(propylene) matrix to study the mechanical properties of natural fiber mat thermoplastics (NMT) in the absence of binder. Film stacking was used as the method of preparation. The results show that hemp‐based NMT have comparable or even higher strength properties as compared with conventional flax‐based thermoplastics. A value of 63 MPa for the flexural strength is achieved at a fiber content of 64 wt.‐%. The influence of the compression ratio on the mechanical properties and density of NMT is also reported. A definite increase in strength is observed with increasing compression together with a much more uniform density profile. In the second part of this study, a unique combination of random hemp fibers, non‐woven mats and poly(propylene) films was employed in film stacking to evaluate strength properties and economic implications. The same fiber content (64 wt.‐%) was maintained in the final NMT by replacing 78 wt.‐% of the mats by random fibers. Preliminary tests reveal better mechanical properties especially in terms of impact energy, which is 50 to 100% higher, as compared with different mats‐only/poly(propylene) combinations. Further, a net saving of 40% in fiber cost is anticipated by replacing 78% non‐woven mats with an equivalent amount of random fibers. Overall results of this study indicate that hemp‐based NMT are promising candidates in automotive applications where high specific stiffness is required. Tensile Strength of different NMTs and GMT. magnified image Tensile Strength of different NMTs and GMT.
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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 | 0.000 |
| 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) | 0.001 | 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".