Development of a motorcycle frame as a problem-based learning experience in design courses for Mechanical Engineering students
Bibliographic record
Abstract
The paradox that exists between the necessity of testing physical prototypes to achieve as much design criteria as possible and the desire to minimize the number of iterations at the experimental validation stage to manage development time and cost, has led the authors to develop a detailed design methodology that guides the engineers and designers through the main activities of the product development process (PDP).The numerical validation activities and the iterations performed at the detailed design phase of the PDP have become key in achieving a product that meets the client needs from a price/performance/reliability perspective. However, before starting the fabrication of a physical prototype, the multidisciplinary team must understand the issues linked to the material behavior under critical conditions of use and in relation with the range of selected processes.In previous papers, a generic methodology that takes into account several design criteria was presented. This methodology was applied to the recreational product industry with the aim of reducing the weight of a roadster frame while controlling its production cost. Specific to vehicle, fatigue and rigidity are amongst the structural criteria that are central to the safety of the user and the handling of the vehicle.The objectives of this paper are thus to outline the advantages of this methodology, show how it could be applied to the structural sub-systems and components of a vehicle, and how it could be integrated in an undergraduate project, taking into account all the design criteria established up-stream in the PDP.As this approach has already been validated in the recreational product industry, it will allow students to converge toward creative, effective but realistic solutions while providing a comprehensive feedback on the client needs.
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 | 0.001 | 0.001 |
| 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".