Lightweight design of steering knuckle structure for vehicles based on topology optimization
Bibliographic record
Abstract
The automotive industry has experienced rapid development in recent years, with a significant increase in the number of vehicles in China. To effectively reduce the energy consumption and carbon emissions of automobiles, implementing lightweight design for the steering knuckle structure is essential. In this paper, in order to prepare steering knuckle parts with less weight, the material of steering knuckle was changed from 40Cr to polyether ether ketone (PEEK), which has less density, while maintaining the performance of the part. Fused deposition molding (FDM) technology, as one of the main methods of thermoplastic material manufacturing, has the ability to machine any complex geometrical structure, which greatly enhances the degree of design freedom. However, in the case of FDM technology, the print parameters determine the performance of the printed sample. Based on this, this paper will explore the influence of process parameters on the mechanical properties of PEEK materials through orthogonal experiments to screen out the optimal parameter combinations for printing. For the printing layer height of 0.1mm, the temperature of the holding chamber is 90 ℃, the filling method for the spiral tetrahedron. The PEEK materials molded under the optimal parameters of FDM were insulated to investigate the effect of the heat treatment process on the mechanical properties of PEEK materials. The loaded condition of the steering knuckle in each working condition is calculated by the basic parameters of the car, the dangerous cross section of the part is determined by simulation, and the structural optimal design of the part is carried out by topology optimization. Under the premise of ensuring its mechanical properties, minimize the amount of material to achieve the goal of lightweight.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 0.001 |
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".