Experimental Investigation to Study the Influence of Variation in Composition on Tribological Behavior and Impact Strength of Aluminium Alloy Al7068
Bibliographic record
Abstract
Aluminium alloys have a wide variety of applications in the industrial sector due to some unique characteristics like lightweight, high strength to weight ratio, corrosion resistance, good electrical conductivity, recyclability, ductility, and formability, etc. Due to this unique combination of properties, the applications of aluminium alloys continue to increase. The tribological behavior and impact strength were studied in this research work by conducting the wear and impact tests, varying the composition of two major alloying elements, Magnesium (Mg) and Zinc (Zn) of Al7068 aluminium alloy. The specimens were prepared as per ASTM standards for wear and impact tests, four compositions each for Mg% varying b/w (2.2 to 3%), and Zn % varying b/w (7.3 to 8.3%). The results of the wear test on the alloy Al7068 shows that the specimens with 3% Mg and 7.6% Zn compositions gives least wear rate at loads 2 kg and 3 kg respectively whereas the specimens with compositions of 2.75% Mg and 7.3% Zn give highest wear rate at a low load of 1 kg. The impact test results indicate that specimens with compositions of 2.2% Mg and 7.6% Zn of the alloy Al7068 give the highest impact strength which in turn improves its performance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".