Multi-objective optimization of GFRP injection molding process parameters, using GA-ELM, MOFA, and GRA-TOPSIS
Bibliographic record
Abstract
Owing to the influence of the injection molding process, warpage and volume shrinkage are two common quality defects for products manufactured by glass fiber-reinforced plastic (GFRP) injection molding. To minimize these two defects, an extreme learning machine optimized with a genetic algorithm (GA-ELM), multi-objective firefly algorithm (MOFA), and a multi-objective decision-making method (GRA-TOPSIS) were implemented in this study. All of the experiments, based on Latin hypercubic sampling (LHS), were conducted using Moldflow software to obtain the results for warpage and volume shrinkage. The prediction accuracy of the defect-prediction models based on the extreme learning machine (ELM) and GA-ELM algorithms were compared. The results show that the GA-ELM models can better predict the defect values. Finally, MOFA was used to find the Pareto optimal front, and the GRA-TOPSIS method was used to find the optimum solution from the Pareto optimal front. According to the results of the simulation verification, the warpage and volume shrinkage were effectively reduced by 12.25% and 6.11%, respectively, compared with before optimization, which indicates the effectiveness and reliability of the optimization method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".