Roll dynamic model and steering stability analysis of the counterbalance forklift truck with considering hierarchical rollover
Bibliographic record
Abstract
The dynamic model of a counterbalance forklift truck is established, and the validations under various operational conditions are studied. The roll motion of the counterbalance forklift is expressed by four degrees of freedom with consideration given to the independent roll motion between the body and chassis, the variation of the roll axis, and the contact between the body limit block and rear axle. Furthermore, testing under turning on firm ground and driving over obstacle conditions are conducted for verifying the dynamic response characteristic of the established model. In addition, numerical simulations are performed to investigate the effect of forklift structural parameters and road roughness excitation on the roll dynamics of forklifts. Finally, the rationality of evaluating the forklift roll state by load transfer ratio (LTR) is discussed. Simulation results are well in harmony with experiment results, suggesting that the proposed dynamic model can be an effective tool for stability analysis of counterbalance forklift trucks. This work provides theoretical foundations for the development of improved anti-rollover control, ultimately leading to enhanced stability and safety of forklifts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".