IBOLT: A Composite Bolted Joint Static Strength Prediction Tool
Bibliographic record
Abstract
The objective of this paper is to describe the bolted joint analysis method developed and used by LM Aeronautics (named IBOLT) including the theoretical basis, required input, and coupon-level strength-prediction validation. IBOLT performs a fracture-mechanics-based static strength prediction for a rectangular composite joint element subjected to any combination of biaxial membrane loads, shear loads, and an off- axis bolt load. Out-of-plane bending moments are also treated. Joint configuration effects (single- or double-shear) are modeled by a beam-on-elastic-foundation analysis to account for the thickness and stiffness of the joint members as well as the bolt bending and shear stiffness. Empirical equations are included to account for fastener head geometry, effects of filled versus open holes, fastener/hole clearance, and fastener torque. Required input, in addition to the usual geometry, loads, fastener, and substrate elastic properties, includes a variety of special laminate-level joint properties. Uni-axial notched, un-notched, and bolted-joint coupon tests are conducted to develop these properties. In addition to these uniaxial coupon tests, bearing/bypass tension and bearing/bypass compression tests have been performed to validate the accuracy of IBOLT strength predictions. An overview of these validation results is provided, along with a brief summary of IBOLT's strengths and weaknesses as a practical bolted joint stress analysis tool.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".