Use of the Taguchi Method for Optimization of Poly (Butylene Terephthalate) and Poly (Trimethylene Terephthalate) Blends through Injection Molding
Bibliographic record
Abstract
Abstract A statistical experimental design method known as the Taguchi method was utilized to optimize the injection molding processes of poly(butylene terephthalate) (PBT) and poly(trimethylene terephthalate) (PTT) blends. Impact strength was taken as the optimized property. The significant parameters included mold temperature, injection pressure, holding pressure, injection time and holding temperature. Results of the Taguchi analysis gave mold temperatures as major influencing factor on the impact strength. The optimal processing conditions were determined through the Taguchi method giving an increase of 13.7% in impact strength for the blend. Further analysis was done to distinguish the blends dependency on temperature. Differential scanning calorimetry curves indicated the presence of recrystallization peaks that were dependent on the temperature profile the sample had received prior to testing. Polarized optical microscopy was used to show the different sphereulitic growth patterns under varying isothermal conditions. It was seen that at 90°C sphereulitic growth contained pockets of different sized spereulites. AFM imaging was also used to indicate differences in blended polymer morphology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".