Improvement in Hardness of LM-6 Aluminum Alloy Green Sand Castings by Taguchi Method
Bibliographic record
Abstract
The green sand casting is most widely and economically used method for past years. The quality of castings and parameters control is very important. With increasing demand for high-quality castings with close tolerances, an attempt has been made in this study to get the optimal setting of the main parameters to improve the hardness of LM-6 Aluminum alloys castings in green sand casting. Five main parameters namely Bentonite clay, Grain fineness no., Moisture, Pouring temperature and Coal dust were identified. The effects of the selected process parameters on the hardness and the subsequent optimal settings of the parameters have been accomplished using Taguchi’s method. L8 (27) orthogonal arrays have been selected and experiments were conducted as per experimental plan given in this array. The results indicate that all the parameters except grain fineness no and coal dust are affecting both the average and variability significantly in the hardness of LM-6 Aluminum alloys castings. The confirmatory experiments have shown improvement in Rockwell hardness to be 6.9%.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".