Quantification of 6-Degree-of-Freedom Chassis Whole-Body Vibration in Mobile Heavy Vehicles Used in the Steel Making Industry
Bibliographic record
Abstract
Whole-body vibration (WBV) of mobile machines used in the steel making industry has not previously been quantified in six-degrees-of-freedom (6DOF). The purpose of this paper was to quantify 6DOF vibrations during the daily operating tasks of 5 commonly used mobile machines types used in the steel making and metal smelting industries. Vibration data were recorded from the chassis of five commonly used mobile machines using a MEMSense MAG3 triaxial accelerometer & gyroscope (MEMSense, SD, USA), and analyzed using custom MatlabTM code. Elevated values were observed at the chassis for crest factors, peak running root mean squared accelerations, and vibration total values, resulting in ISO 2631–1 (1997) comfort predictions ranging from Uncomfortable to Extremely Uncomfortable. Vibration dominant frequencies were generally between 1 and 8Hz. A second peak which occurred at approximately 27 Hz was observed for each vehicle in almost all axes. Occurring at a frequency that has the potential to produce negative health effects, this second peak was probably caused by the engine idling or running at low speeds. Field vibration profiles from this study have been used as inputs to a 6DOF robot for use in a corresponding laboratory study designed to optimize seat selection thus allowing the steel making and other similar industries to select operator seats based on industry specific field vibration characteristics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".