Multi-objective optimisation of plastic injection moulding process using mould flow analysis and response surface methodology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Concurrently maintaining a stable part weight and high production rate has remained a challenge in injection moulding. As a statistical tool, response surface methodology (RSM) was exploited to examine effects of process parameters on part weight and production rate. The objective was to optimise process parameters in order to obtain weight stability at high rates of production. The study took advantage of validated numerical simulations using MoldFlow to generate input data required in statistical analysis. Analysis of variance revealed that packing time has a consequential impact on both responses, where an increase in packing time resulted in high part stability, but a low production rate. Real-scale test using optimal parameters producing the best trade-off between part weight and production rate was performed to validate efficiency of the optimisation procedure. The part weight and production rate predicted by RSM were in good accordance with experimental observations, with relative errors of less than 2.5%.
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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 | 0.001 | 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) | 0.000 | 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 it