DLFT experiments with cyclic butylene terephtalate
Bibliographic record
Abstract
Cyclic oligomers have low molecular weights and therefore exhibit very low viscosities (in the order of 20 mPa.s) in contrast to the resulting high viscosities related to high molecular weightsobtained after polymerization. This characteristic is very appealing for the production of polymer composites since by taking advantage of the low viscosity, fibres can be thoroughly wetted by the cyclic oligomer before starting the reactive process of polymerization. As a result, a homogeneous mixture with a uniform volumetric distribution of reinforcing fibres in the polymer matrix is expected with the use of appropriate processing equipment. Cyclic butylene terephthalate (CBT®) has been successfully polymerized into poly(butylene terephthalate) (PBT) using a variety of tin or titanate-based initiators. An exploratory project has been initiated by NRC at the Magna-NRC Composites Centre of Excellence (MNCCE) with the participation of Cyclics Corporation (Cyclics) as the supplier of CBT for trials using the D-LFT process. The goal is to assess a number of aspects involved in the simultaneous reactive extrusion/compounding process using CBT fibreglass reinforcement in a D-LFT process in order to determine the adequate processing conditions to produce parts. This paper presents a description of the experiments conducted up to date and the results obtained thereby. Parts were produced using a test specimen mould with a shot-pot device and a storage tub for automotive applications. Samples were characterized for tensile, flexural and impact tests. Density measurements were taken at different points of the tub to examine the uniformity of the composite material obtained. Plaques moulded with the test specimen mould showed good mechanical properties which exceed typical values for PP with 40% glass composites. The experiments proved the feasibility of the use of the open ring polymerization process in a D-LFT system. A set of operating conditions including temperature profile, screw speed and feed rate was established to achieve the adequate residence time and material temperature history for the effective processing of CBT in the D-LFT system.Mechanical properties measured from the moulded part (tub) showed significantly lower values than those for the test specimen mould. These were related to incomplete packing of the material in the cavity of the mould, the high residence time required and non-optimal shot size.High variability within the parts and between parts produced is thought to be a consequence of the processing difficulties for the tub mould.
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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) | 0.002 | 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 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".