Multi-objective optimal gating and riser design for metal-casting
Why this work is in the frame
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Bibliographic record
Abstract
The gating and riser design plays an important role in the quality and cost of a metal casting. Due to the lack of existing theoretical procedures to follow, the design process is normally carried out on a trial-and-error basis. In this paper, the casting design is first formulated as a multi-objective optimization problem with conflicting objectives and a complex search space. An optimization method using multi-objective evolutionary algorithm (MOEA) is developed to overcome such complexities. A framework for integrating the optimization procedure driven by data for the design evaluation is then presented. The proposed optimization framework is applied to the gating and riser design of a sand casting. It is shown that the MOEA method yields good results and provides more flexibility in decision making.
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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 | 0.000 | 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 | 0.001 |
| 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