Light Weight Structure Development Using Non Linear Load Cases For Suspension Components (Cradle)
Bibliographic record
Abstract
Based on current trends, there is a huge demand for lightweight components, which improves fuel efficiency and reduces cost of the vehicle. Stiffness based optimization process is simple and straightforward while durability (Misuse load case) based optimizations are relatively complex due to its highly nonlinear behavior. However, durability performances are critical in a front cradle design. So a process needs to be identified for creating a new light weight front cradle design. This study talks about the process of identifying new cast aluminium cradles achieving NVH and durability performance. Load path study using topology optimization is done based on compliance method for the durability load case. A concept model is generated from the topology results. This concept model is further optimized for thickness of ribs and walls by the application of various shape variables. All the critical non linear durability load cases are linked for the shape optimization study. ANSA is used to create the required shape variables and Isight is used as the optimizer. 20% weight reduction is achieved using this process satisfying all the performance targets.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".