Integration of 3D Scan Data into the Finite Element Analysis Workflow for Simulation of Rotorcraft Components
Bibliographic record
Abstract
A process for integrating 3D scan data into the finite element analysis (FEA) workflow is presented. The process was developed to utilize CAD and/or scan-based geometry to create a 3D Finite Element model for structural analysis. Three significant phases of the process include surface acquisition, geometric refinement, and FEA evaluation. The scan-based process provides a cost effective method to capture the surface geometry of physical components and permits a digital representation of the article to be created for analysis purposes. It is shown that the role of alignment, deviation checking, and prior analysis are significant factors in efficiently developing models for FEA. The application of scan-based FEA was highly effective in simulating component repairs, weldments, and predicting mechanical test behavior. Results from these applications were substantiated by digital image correlation and strain gauge data, showing excellent correlation between 3-10%. When comparing scan-based FEA results it was found that accounting for paint thickness can have a significant effect on the accuracy of the results. With a successful scan-based FEA approach developed, numerous applications of the process resulted in reduced scrap rate, supported safety of flight testing, and ensured continued on-time delivery of aircraft.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".