Morphological and mechanical comparision of injection and compression moulding in-line compounding of direct long fibre thermoplastics
Bibliographic record
Abstract
Long fibre thermoplastics (LFT) based on polypropylene/glass fibre (PP/GF) composites has become one of the most widely used plastics in semi-structural and structural automotive applications in both aesthetic and non-aesthetic parts. LFT are commercially available in pre-compounded pellets for injection moulding and are developed with specific properties for targeted functions. In a rationalizing effort to reduce costs, heat histories, and create in-house flexibility of material blending in-line compounding (ILC) of base materials including resin, additives (heat stabilizers, colors, coupling agents, etc.), and glass roving reinforcements for direct moulding of LFT parts (D-LFT) has been developed in the last 10 years. Two major versions of D-LFT technology currently exist on the market, both relying on twin-screw extrusion (one-stage or two-stage) for ILC, one utilizing compression moulding and the other injection moulding. While these two technologies share several similarities, they also present significantly different features in terms of fibre length, orientation and mechanical properties for example, related to their respective processing conditions. The objective of this paper is to address some of them. A Dieffenbacher LFT Direct system, using the compression moulding process, and a Krauss-Maffei Injection Moulding Compounder (IMC), using the injection moulding process, were used to mould similar test parts that have a significant level of complexity in their geometry. Samples were taken from the parts and from machine purges for a comparison of the respective fibre distribution patterns of the two moulding technologies using micro focus X-ray computed tomography. A characterization of their fibre length distribution was also performed on these samples from pyrolysis and image analysis. Resulting mechanical properties were then added to the comparison matrix to provide a comprehensive picture of the two moulding technologies.
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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 | 0.001 | 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) | 0.002 | 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".