Investigation of Vibration Characteristics of Stir Cast Aluminum Reinforced SiC Composite Beam
Bibliographic record
Abstract
In various applications such as plant mines or mechanical systems, machines in operation generate significant vibrations which are transmitted from the foundation to the surrounding environment and precision instruments.In order to minimize these vibrations, they must be either isolated, absorbed, or damped.The current study investigates the effects of an oil damper, passive vibration isolator, and absorbers (single and double) on the vibration frequency of a composite beam made of Al6061 (with 90% weight fraction) and SiC (with 10% weight fraction) produced through the stir casting process.Experimental analysis was conducted on the fabricated composite beam to investigate the frequencies of the fundamental vibration modes under hinged-hinged, fixed-free, and fixed-hinged boundary conditions.Results indicate that the absorber, damper, and isolator have a significant effect in reducing the amplitude of vibrations, resulting in lower frequencies compared to the main frequency.Furthermore, as the number of holes in the beam increases, the frequencies decrease due to a decrease in stiffness.The frequencies are higher for fixed-free end conditions without holes compared to other conditions.The oil damper was found to be more effective in reducing vibrations compared to absorbers and isolators.
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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 | 0.000 | 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".