Multi-objective optimization of western bentonite (Al<sub>2</sub>H<sub>2</sub>Na<sub>2</sub>O<sub>13</sub>Si<sub>4</sub>)-blended green sand casting process parameters to improve mould quality
Bibliographic record
Abstract
The quality of the mould in the green sand casting process plays a vital role in achieving good quality castings. In this research work, the mould properties permeability and hardness are focused on to improve the quality of the mould. These properties are improved by optimizing the process parameters such as degrees of ramming, mould wall thickness, and weight percentage of western bentonite using Taguchi-based grey relational analysis. The optimal level of these process parameters is experimentally validated by producing castings in a modern jobbing foundry. In the validation experiments, the moulds are produced with nominal hardness (92) and the mould wall thickness is reduced by creating cavities for storage of gases at a distance of 30 mm from the mould cavity. An interesting result found is that the nominal mould hardness and reduced wall thickness improve the quality of the mould by increasing the permeability value from 80 to 120. The improved mould quality reduces the mould-related casting rejection rate in pressure plate casting from 40% to 30%.
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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 | 0.000 |
| 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".