Bibliographic record
Abstract
In an ongoing search for better vehicle fuel economy, the automotive industry has put significant emphasis on the reduction of vehicle weight while retaining stringent safety, quality and performance standards. With its high specific stiffness, strength, and fatigue performance under typical automotive service conditions, forged magnesium is a potential material to fill these requirements. Investigating an existing front lower control arm, engineering specifications were developed to evaluate the performance of a forged magnesium replacement. Combining a design volume derived from a kinematic CAD model and the produced engineering specifications, an optimization design space was created, and a component optimized within it using Altair Optistruct. Based on this optimized result an initial design was created in CAD, and design-analysis iterations conducted until it was structurally equivalent to the baseline design. This initial detail design produced a mass savings of 39% over the benchmark cast aluminum control arm, and only failed to challenge it in fatigue. It is expected that future designs will improve fatigue performance with little added mass, while continuing to integrate improving knowledge about the forging and mechanical performance of magnesium alloys.
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.
How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".