Safety and Product Robustness in the Air Vehicle Design Process
Bibliographic record
Abstract
This paper discusses the application of the Design Failure Modes and Effects (DFMEA) process as applied within the Airframe and Mechanical Systems (AF&MS) design organization at Sikorsky Aircraft, a Lockheed Martin Company. The DFMEA process, an adaptation of the SAE J1739 Surface Vehicle FMEA standard, is tailored to meet the helicopter airframe design application for DFMEA and is further modified for a bottoms-up abbreviated Design Failure Modes and Effects method, the A-DFMEA, which is a design engineer conducted assessment versus the top down DFMEA team approach. While this method may have certain shortfalls in that only a single individual is conducting the preliminary assessment, it does have the benefit of increasing the design engineer's awareness and focus on potential areas of concern such as various failure modes by component type which can be addressed early in the design or monitored as the design matures through the product design/development cycle. The paper will present a process flow, risk assessment scoring methodology as well as DFMEA and A-DFMEA scoring worksheet, remedial actions, and mitigations from those results. Lastly, a discussion of how those results enter into the digital thread and data archiving are presented.
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".