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
In actual engineering, the drive axle of vehicles is often enlarged to prevent it from being damaged. However, the enlargement will increase the weight of the vehicle, pushing up fuel consumption and exhaust emissions. This common practice is obviously detrimental to the environment and sustainable development. To meet the stiffness and strength requirements on the drive axle housing of Steyr heavy trucks, this paper carries out finite-element analysis on the stiffness and strength of the axile housing under different working conditions, in the light of its actual stress features. According to the production process of drive axle housing in truck, the authors reviewed the development of the materials for high-strength axle housing, which could be properly formed through hot stamping, cold stamping, and mechanical expansion, and briefly introduced the structural features of drive axle housing. Then, a drive axle model was established in the three-dimensional (3D) drawing software Pro/ENGINEER, and converted into a finite-element model in Pro/Mechanica by calling the meshing command. On this basis, the static load of axle housing was analyzed under four working conditions: maximum vertical force, maximum traction, maximum braking force, and maximum lateral force. Finite-element analysis was performed on the meshed model to obtain the displacement and stress cloud maps of the axle housing under each working condition. The results show that the drive axle housing satisfy the requirements on strength, stiffness, and deformation. To sum up, this research improves the design efficiency and quality of products through finite-element analysis on the stiffness and strength of drive axle housing.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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