Fast computation of preloaded bolted circular joint aiming at fatigue bolt sizing
Bibliographic record
Abstract
Purpose This paper seeks to deal with a new modelization method which aims at fatigue sizing of preloaded bolted joints. Industrial design offices indeed need new models which, on the one hand, take bending of the bolts and geometrical non‐linearity into account and, on the other hand, run fast enough to be used for preliminary design stages. Usual sizing procedures derive from VDI recommendations, which makes them inaccurate. On the contrary, classical finite element methods are revealed to be very costly. Design/methodology/approach The first task lies in reducing the physical problem down and model the structure using axisymmetrical elements. Then, the core of the method lies in modifying the stiffness matrix of a tube element, in order to modify the axial compression stiffness to the one used by preloaded assembly classical computations. Eventually, a 2D finite element model is programmed which takes advantage of the modified element. A mounting was built to reproduce the typical loading of a slewing bearing. Experimental tests were carried out in order to help analyse the problem and to check finite element simulation results. Findings Sample experimental results are presented which confirm the need for new models and validate the 2D model that was developed. Research limitations/implications The new finite element, as well as the set of hypotheses that are used, appear to be usable for other bolted joints. Practical implications A software was produced for the industrial partners, which is usable by non FE‐specialists. Originality/value This work may serve as a basis for building fast and accurate finite element models of other types of bolted joints.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".