Optimization of Process Parameters for Preparation of Lanthanum Hexa-Aluminate Powders Using Combinatorial Approach of Taguchi-GRA and ACO Methods
Bibliographic record
Abstract
This work focuses on selection of optimal process parameters for the preparation of Lanthanum Hexa-aluminate (LHA) nanoparticles using chemical precipitation and filtration process. Multi response optimization is performed using Taguchi-GRA combinatorial approach using the process parameters such as Temperature (A), Time (B) and Composition (C). The results showed that % composition has the largest effect on hardness, while the Calcination Temperature is the most important factor in ultimate compression strength. In GRA analysis, the combined effect of hardness and ultimate compression strength is considered and the optimum combination is identified (A1B2C2). The percentage of the contribution was most important factor affecting hardness performance (36.58%). Based on the GRA results a regression equation is generated and optimized using ACO technique followed by preparation and characterization of powders. For the powders, prepared FESEM/EDS analysis were done and observed that average grain size of the particle is 85nm.
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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 | 0.000 | 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".