Generic Product Development Process at the detailed design phase
Bibliographic record
Abstract
The diversity in the types of products on the market, leads us to agree that for each type of product manufactured, there is a specific methodology and a corresponding product development process. Each company may indeed have different development and design methodologies, to suit their specific needs. The complexity of the product, the competitiveness of the market and the pace of change in technology, are all factors that model the design and development processes. Remaining competitive requires from companies that they use design methodologies and production techniques that enable them to design and manufacture their products in the shortest possible time. The methodology that we have developed will focus on the achievement of some design and validation criteria (fatigue, ultimate strength, stiffness, elastic limit, etc.) The proposed methodology features four phases: 1) Data collection, 2) Analysis / Optimization, 3) Design review, 4) Validation. The first two steps represent the quantitative dimension (theoretical) of the methodology in which several alternatives are developed to meet the design criteria. Steps 3 and 4 represent the qualitative dimension (choice and validation of final solution). More specifically, the choice of a solution among several alternatives will be taken in step 3 before starting the prototyping work. In the validation phase (step 4), the designer will select the criteria and validation tools, as deemed appropriate. The methodology will also include a section on the economic impact of the price / performance ratio therefore enabling engineers to make the best decisions regarding the key design parameters (geometry, material, design). Iteration loops will provide an efficient tracking mechanism for all the parameter changes, following the theoretical analysis and physical testing. The methodology will be applied on two different types of products (new concept and design as an evolution of an existing model)
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".