Design & Validation Methodology applied to a roadster frame based on life prediction
Bibliographic record
Abstract
The recent increases in gasoline price have initiated a new thrust to reduce vehicle weight, hence creating a new market opportunity in the recreational product industry. At the same time, integration of both optimization and validation at the detailed design phase into the product development process has become key to achieving a product that meets the client needs from a price/performance/reliability perspective. Such integration also leads to more accurate requirements regarding the behavior of the structural components of a recreational vehicle. Therefore, to reach the objective of a weight reduction for the structural subsystem in a three wheels roadster project, a methodology that optimizes both frame geometry and material properties according to the following types of design criteria has been developed: - Structural criteria in order to support the specified loads;- Weight and cost criteria to assess some performance and market targets;- Qualitative criteria such as aesthetic, assembly or manufacturing. One of the objectives of this paper is to outline a design and validation methodology that could be applied to the structural sub-systems of a recreational product with regard to all the design criteria established up-stream in the product development process. This approach will converge into a creative, effective and profitable solution and will allow designers to offer a feedback on the client needs. The optimization process that the approach entail allows finding one or more combinations of parameters maximizing or minimizing a given design criterion, while the validation activities provide feedback to the designers in order to verify the calculations accuracy and the respect of all design criteria. According to the nature of the load cases identified, the proposed methodology has been applied to design and validate the frame of a three wheels roadster as part of a challenging multidisciplinary project involving students, professors and engineers from the recreational products industry.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".