Kineto-dynamic design optimisation for vehicle-specific seat-suspension systems
Bibliographic record
Abstract
Designs and analyses of seat-suspension systems are invariably performed considering effective vertical spring rate and damping properties, while neglecting important contributions due to kinematics of the widely used cross-linkage mechanism. In this study, a kineto-dynamic model of a seat-suspension is formulated to obtain relations for effective vertical suspension stiffness and damping characteristics as functions of those of the air spring and the hydraulic damper, respectively. The proposed relations are verified through simulations of the multi-body dynamic model of the cross-linkage seat-suspension in the ADAMS platform. The validity of the kineto-dynamic model is also demonstrated through comparisons of its vibration transmission response with the experimental data. The model is used to identify optimal air spring coordinates to attain nearly constant natural frequency of the suspension, irrespective of the seated body mass and seated height. A methodology is further proposed to identify optimal damping requirements for vehicle-specific suspension designs to achieve minimal seat effective amplitude transmissibility (SEAT) and vibration dose value (VDV) considering vibration spectra of different classes of earthmoving vehicles. The shock and vibration isolation performance potentials of the optimal designs are evaluated under selected vehicle vibration superimposed with shock motions. Results show that the vehicle-specific optimal designs could provide substantial reductions in the SEAT and VDV values for the vehicle classes considered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".