Optimization of powder metallurgy parameters of TiC- and B<sub>4</sub>C-reinforced aluminium composites by Taguchi method
Bibliographic record
Abstract
In this work, an aluminium-based metal matrix composite material was developed via powder metallurgy considering various input process parameters. Sintering time, sintering temperature, and compaction pressure were the three main factors used as input process parameters, which were varied at three levels. The research was planned with reference to the experimental design of an L9 orthogonal array using a 3 × 3 matrix. The density, Vickers hardness, and compression strength were tested and analyzed. The influence of individual input parameters were analyzed using the Taguchi-based S/N ratio and analysis of variance (ANOVA). The optimum parameter levels to achieve low density, high hardness, and high compressive strength were identified through main effect plots. Experimental results indicate that the sintering temperature and compaction pressure strongly influence properties such as density and hardness. Similarly, compression strength depends mainly on sintering time and sintering temperature. Through ANOVA analysis, the optimum levels were confirmed for the process parameters sintering time, compaction pressure, and sintering temperature to produce the most favorable metal matrix composite material.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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".