Product Modeling, Evaluation and Validation at the Detailed Design Stage
Bibliographic record
Abstract
The expansion of the markets corroborated with product customization and short time to launch the product have led to new levels of competition among product development companies. To be successful in the globalization of the markets and to enable the evaluation and validation of products, companies have to develop methodologies focused on lifecycle analysis and reduction of product variation to obtain both quality and robustness of products. Keywords: Modeling, Evaluation, Validation, Design ProcessThis paper proposes a new design process methodology that unifies theoretical results of modeling stage and empirical findings obtained from the validation stage. The evaluations and validations of engineering design are very important and they have a high influence on product performances and their functionality, as well on the customer perceptions.Given that most companies maintain the confidentiality of their product development processes and that the existing literature does not provide more detailed aspects of this field, the proposed methodology will represent a technical and logistical support intended for students or engineers involved in academic as well as industrial projects.A generic methodology will be refined based on a new approach that will take into consideration the specification types (quantitative or qualitative), the design objectives and the product types: new/improved, structural/esthetic. Hence the new generic methodology will be composed of specific product validation algorithms taking into account the above considerations. At the end of this paper, the improvements provided by the proposed methodology into the design process will be shown in the context of the engineering student capstone projects at the Université de Sherbrooke.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".