Metodik för krocksimulering av ryggstöd på truck : En jämförelse mellan de två lösarna OptiStruct och RADIOSS
Bibliographic record
Abstract
Toyota Material Handling is evaluating the feasibility of adding a backrest to one of its pallet trucks to improve driver safety when reversing and for ergonomic benefits. According to standards, the backrest must withstand certain permanent deformation when colliding with a beam rack at 1.6 km/h. To minimize costs, reduce time, minimize environmental impact, and mitigate safety risks, this study investigates a virtual methodology for crash testing. The aim is to develop a suitable material model for the backrest and evaluate which of the two solvers, OptiStruct and RADIOSS, is most ideal for a crash simulation. A physical bending test is conducted, and the data is used to calibrate a Johnson-Cook material model. The backrest is then simulated in three increasingly complex finite element models (simple, semi, and advanced), and results are compared to physical tests for validation. Finally, the calibrated models are used to simulate the forklift colliding with a beam rack, and this is evaluated against an existing physical pendulum test. Both solvers are comparable in usability, but the advanced model with the Johnson-Cook material model, solved using RADIOSS, offers results closest to the physical pendulum test. However, significant deviations in permanent deformation remain, and possible error sources are discussed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.024 |
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".