Analysis of an automobile door closure vibroacoustic response
Bibliographic record
Abstract
The acoustic response of a car door latch has been shown to directly impact the customers’ perceived quality and value evaluation of the automobile. This work introduces an experimentally validated computational model of three door latch components. The transient sound pressure level response of the three door latch components during door closure was collected in a semianechoic chamber using a three-element condenser microphone array. Postprocessing methodologies such as sound pressure level versus 1/3 octave band and continuous wavelet transform analysis were performed. This provided an in-depth analysis on the overall acoustic response and identification of dominant frequencies corresponding to four specific impact events during latch operation. Computational finite element analysis of the closure system using a rigid body, and explicit dynamic and transient structural acoustic analyses provided additional insights into the latch component interactions and the acoustic response generated empirically. Recorded average sound pressure level, frequency decomposition, and impact reaction forces are presented in addition to a comparison between the acoustic response for two different door closure speeds. It was found that an increased door closure speed increased the response sound pressure level, decreased damping of the primary impact, and decreased the frequency bandwidth of the response, thereby generating an acoustic response that would be perceived as noisier, less safe, and less secure by customers. These findings provide additional insights into the primary impact acoustic response of an automotive door latch during closure. The methodology introduced in this work allows automotive engineers to perform future work with modified latch components to further improve the psychoacoustic response of the automotive car door latch, further increasing the value evaluation of the automobile.
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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".