GENERIC PRODUCT DESIGN & VALIDATION METHODOLOGIES AT THE DETAILED DESIGN STAGE
Bibliographic record
Abstract
Doing design is to imagine and specify things that don’t exist, with the scope of modeling them and bringing them into the world. The «things» may be palpable-machines, buildings and bridges; they may be procedures-design methodologies for an organization or protocols for a manufacturing process, or for solving a scientific research problem by experiment; they also may be works of art-painting, lyrics, music or sculpture. Engineering design can be challenging and exciting, or it can be taxing, difficult and unproductive if the validation methods of the product are not linked to the client needs and to the product specifications. Uncertainties and variability always exist in design predictions. Loads are often variable and inaccurately known, strengths are variable and sometimes inaccurately known for certain failure modes or certain states of stress and other uncertainties may result from variations in the quality of manufacture, operation conditions or maintenance practices. One of the objectives of this paper is to outline a methodology that highlights the exciting challenges of product design and allows both engineers and students to focus on the development of a creative, effective and profitable solution. Another challenging goal of this paper would be to integrate design optimization and design validation at the detailed design phase in the product development process. Detailed design involves interactions between three elements: geometry, materials and loads. In this context, links between these elements will be formalized in terms of design methodologies. The optimization process 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 achievement of all design criteria. To provide safe, reliable operation in the face of these variations and uncertainties, it is common practice to utilize the design safety factor and to integrate it into the product development process (PDP).
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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".