Selecting seats for steel industry mobile machines based on seat effective amplitude transmissibility and comfort
Bibliographic record
Abstract
OBJECTIVE: The purpose of this work was to help a steel industry partner select the most appropriate of three high end heavy equipment seats to retrofit a number of their heavy mobile machines used in the steel making process. PARTICIPANTS: The participants included 8 males (22.3 ± 2.0 yrs.) and 8 females (23.5 ± 1.8 yrs.) with no experience operating heavy mobile equipment. METHODS: Previously recorded 6-DOF chassis acceleration data from a Pot Hauler (a machine which picks up and transports pots of slag) were used to extract six, 20 second representative profiles for implementation on a lab-based heavy machine simulator (6-DOF Parallel Robotics System Corporation robot). Subjects sat on three heavy equipment seats (BeGe7150, Grammar MSG 95G1721, and a 6801 Isringhausen with the seat pan cushion retrofitted with a Skydex cushion) mounted on the simulator. Each subject completed three trials for each combination of seat (n=3) and vibration profile (n=6). Chassis and operator/seat interface vibration were measured by 2, 6-DOF vibration transducers. Variables included Seat Effective Amplitude Transmissibility (SEAT) (X,Y,Z,Roll,Pitch,Yaw,6DOF Vector Sum) to determine if the seat was attenuating or amplifying the vibration, 6-degree of freedom (DOF) vibration total value weighted predicted comfort (Avc) (according to ISO 2631-1) and operator reported comfort (ORC). RESULTS: Factorial ANOVAs revealed significant differences (p < or = 0.05) between seats for all SEAT variables but different seats performed better than others depending on the axis. Significant differences between males and females were observed for SEAT in X,Y, and Pitch as well as for Avs. As expected there were significant differences between vibration profiles for all assessed variables. A number of interaction effects were observed, the most frequently occurring of which was between seat and vibration profile. CONCLUSIONS: Based upon the number of seat and vibration profile interactions, results suggest that a single seat is not suited for all tested conditions. However, SEAT values for all of the seats tested were extremely low (e.g., 6-DOF SEAT < 30%) indicating that all of the seats were capable of providing good vibration attenuation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".