An experimental investigation of noise emission from a vehicle gearbox system
Bibliographic record
Abstract
Nowadays, one of the most valuable criteria of vehicle quality assessment is based on acoustic emission levels: A car is judged comfortable Depending on the noise level transmitted inside. Consequently, there is a general attention to the design criteria aimed at improving the structural-acoustic behavior, to comply with the increasingly restrictive ergonomic standard. The aim of this paper is to study experimentally the noise in terms of Sound Pressure Level (SPL) generated from a lab single-stage helical gear system simulating the actual vehicle gearbox. A simplified test rig was designed with the necessary measurement equipment. Such investigation can be applied to provide the acoustic engineer with the necessary information to ensure that the design satisfies performance specifications and regulations imposed by governments and standard bodies. The subsequent step is to redesign such components responsible for intolerable emissions, without the need for an extensive prototype effort. The results indicate that the resonant frequencies particularly those related to gear meshing and rotating shafts frequencies must be considered in addition to structure rigidity resonance frequencies if any reduction for gearbox noise occurred from the friction between the teeth, poor surface finish on the mating parts, an imperfection in the tooth profile or a transmission error is required. Key words: Noise emissions, structural acoustic, emissions regulations, helical gear, design criteria, multi-channel analyzer.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".