Load Development and Automation for Composite Bolted Joint Bearing and Bypass Analysis
Bibliographic record
Abstract
This paper discusses a standardized methodology that has been defined for extraction of load data from a coarsely meshed finite element model to be input to bearing-bypass analysis of composite bolted joints. The Lockheed Martin Aeronautics in-house tool, IBOLT, uses a fracture-mechanics-based static strength analysis methodology to predict bearing-bypass capability and requires input of balanced freebody loads. Because bearing-bypass analysis margins are sensitive to load input, development of accurate loads according to a reliable standard methodology is critical in component design. The standardized methodology discussed here ensures consistent development of loads for a critical analysis in composite structures. The methodology has been captured in an automation tool, BIBOLT, which can be used to rapidly define, execute, and post-process large numbers of bearing-bypass analyses of composite bolted joints. I. Introduction This paper describes a standardized methodology for bearing and bypass analysis of bolted joints in composite materials. The methodology uses standardized freebody definitions to determine loads from finite element results that are used to drive a Lockheed Martin Aeronautics in-house analysis tool called IBOLT to calculate bearing and bypass margins. This standardized load extraction and analysis methodology has been automated in another inhouse tool called Batch IBOLT (BIBOLT), which dramatically speeds execution of high-volume bearing and bypass analysis. The paper first provides background discussion on the nature of composite bolted joint failure modes and analysis. Next it discusses the role of finite element models in load development for composite bolted joint analysis. Finally, the paper describes the capabilities and application of the automation tool that has been developed for executing large numbers of composite bolted joint analyses.
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".