MULTIDISCIPLINARY DESIGN ANALYSIS AND OPTIMIZATION FRAMEWORK FOR REGULATORY DRIVEN MEDICAL DEVICE DEVELOPMENT
Bibliographic record
Abstract
Abstract Multidisciplinary design optimization (MDO) is a technique used in the design of systems involving the integration of many disciplines. The architecture and formulation of MDO has an impact on the solution time and optimality of final designs. The process of developing medical devices requires the combination of medical and technical knowledge and abilities. Developing a medical device is done by a complicated collection of Product Development Processes that entail tremendous oversight to ensure conformity to regulatory requirements. Regulatory standards often provide stern “Go / No-Go” policies which may discretize the design variables further increasing the complexity of the optimization problem. This work proposes a novel design approach which utilizes systems engineering practices to undertake complex multidisciplinary design optimization while implementing regulatory guidelines for medical devices. The formulated model is then applied and examined in a case study towards the development of a piezoelectric respiratory sensor. It is observed that the novel framework would extensively improve the design space definition and process driven product development practices.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".