Metodik för krocksimulering av ryggstöd på truck : En jämförelse mellan de två lösarna OptiStruct och RADIOSS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it