Effect of Uncarbonized Eggshell Weight Percentage on Mechanical Properties of Composite Material Developed by Electromagnetic Stir Casting Technique
Bibliographic record
Abstract
This study deals with the effect of uncarbonized eggshell weight percentage on the tensile strength of AA 2014 composite manufactured by Electromagnetic stir casting technique. Response surface methodology (RSM) is used to design the experiment. The selected process parameters are preheat temperature, stirring current, stirring time, matrix pouring temperature, and reinforcement of uncarbonized eggshell weight percentage. Analysis of variance (ANOVA) is employed to study the influence of selected process parameters. The maximum tensile strength of 287.194 MPa was achieved for reinforcement preheat temperature 537.87 C, stirring current 12 A, stirring time 179.9 sec, matrix pouring temperature 726.8 C, and reinforcement weight percentage of 12.46. At optimum level of process parameter confirmation tests are performed to validate the result. In confirmation test, tensile strength and hardness are investigated. It is observed that due to the reinforcement of uncarbonized eggshell in AA2014 aluminium alloy, tensile strength and hardness are improved by 55.24 % and 39.58 %.
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 | 0.000 | 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 | 0.000 |
| 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".