Wear and Residual Stress Analysis of Waste Sea Shell and B4C Particles Reinforced Green Hybrid Aluminium Metal Composite
Bibliographic record
Abstract
Present work, evaluates the effects of Sea shell and B4C powder on the mechanical behavior of the aluminium material (Al 6082). Stir casting method was used to fabricate a hybrid composite of Al 6082 with sea shell and B4C. A linear reciprocating tribometer was used to evaluate the wear and friction behavior. The addition of sea shell and B4C particles, resulted in 7-13 % reduction in coefficient of friction and 32-43 % improvement in wear resistance as compared to the Al 6082 alloy. The average Vicker hardness was also improved by 20-70 %. The residual stresses developed during the mechanical testing were also measured to inspect the generation of residual stresses in the fabricated composite. Optical micrographs and scanning electron microscope (SEM) were obtained to analyze the prepared composites for the wear behavior. Waste sea shells were reinforced with B4C in Al 6082 alloys. Microhardness along with microstructure and residual stress of the developed green hybrid aluminium metal composite are compared and presented. The wear and friction data have also been shown in this paper for the use of green hybrid aluminium composite in tribological applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".