A two-node nonlinear connector for simulating simplified models of bolted joints under extreme loads
Bibliographic record
Abstract
Abstract In industrial applications, fine-scale simulations of bolted joints are often impractical due to the numerous nonlinearities in the vicinity of the bolt, which result in computationally expensive calculations, particularly during the early design stages. To address this, engineers typically replace detailed bolt models with simplified models built from a connector library available in commercial finite element (FE) solvers. This paper presents a nonlinear FE connector model, along with an identification methodology, designed to capture the full behaviour of a bolted assembly. The model is driven by key design parameters, such as bolt preload, friction coefficients, or elastoplastic properties of the materials used. The connector formulation separates the various mechanisms that influence the macroscopic behaviour of bolted assemblies. Axial behaviour is modelled by accounting for the effects of preload and axial stiffness, while tangential behaviour incorporates friction between the assembled plates, under the bolt head or nut, as well as plasticity in the bolt and potential contact between the screw and bore in case of extreme loads. The parameters for this connector are identified using a generic overlap joint. The connector model is implemented through a user-defined element subroutine in Abaqus/Standard ™. Comparative analysis of quasi-static responses from fine-scale full 3D simulations and those using the proposed connector across different bolted assemblies shows close agreement, with a significant reduction in computational time.
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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".