Surface Defects of TPO Injected Foam Parts for Automotive Applications
Bibliographic record
Abstract
Abstract Benefits of reduced vehicle weight can have an important environmental impact since there is a 6 to 8% improvement in fuel efficiency for every 10% in weight reduction. In this work, foaming technology is used to produce injection molded TPO parts (thermoplastic olefin compounds) for the automotive industry with a minimum of 20% weight reduction while retaining a glossy surface quality. It was found by Guo et al. (2006, 2007) that the best strategy to raise the surface quality was to decrease the shot size, the cycling time and the temperature of the injected melt. However, the targeted objective of a minimum of 20% weight reduction was not achieved in these preliminary experiments. Therefore, in this work a blend of TPO and maleic anhydride modified polypropylene (PP-g-MA) was used in order to promote TPO foamability and improve the surface quality of injected parts. Under these conditions, we managed to produce injected molded TPO samples with a 24% weight reduction and good surface quality. Moreover, we investigated the effect of PP-g-MA in the TPO system by performing rheological measurements and photoacoustic Fourier transform infrared (PA-FTIR) analysis to characterize the PP-g-MA physico-chemical interactions with TPO.
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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".