Optimized Bolt Tightening Sequences in Bolted Joints Using Superelement FE Modeling Technique
Bibliographic record
Abstract
A lot of effort is put to achieve bolt preload uniformity during the assembly process of offshore bolted joint connections resulting in potentially high economic costs and project delays. The complexity of this operation is due to the effect of the elastic interaction between the different joint elements which causes load variations of adjacent bolts whenever a bolt is tightened. As a consequence, it is difficult to achieve a uniform target load in the bolts. In order to avoid this phenomenon, tightening sequences of a large number of passes are usually carried out until a uniform target load is achieved. This solution is neither practical nor efficient when treating hundreds or even thousands of bolted joints due to the large assembly time needed. Several methods were developed to study the effect of the elastic interaction and minimize the assembly time. These methods usually predict the loss of load of every bolt during the tightening sequence, and thus calculate the tightening loads that will provide a uniform final load at the end of the sequence. As a result, an optimized tightening sequence is achieved, which provides a uniform final load distribution in only one or two tightening passes. However, several complex and costly analyses are previously necessary for such purpose. Based on these traditional methods, this paper presents a new and more efficient optimization methodology to achieve assembly bolt load uniformity. The method is based on the use of superelement technique and is capable of producing similar results with computational costs reduced by 30 times as compared to the more conventional Finite Element (FE) modeling. The results were satisfactorily validated with the latter as well as with tests conducted on a NPS 4 class 900 bolted joint.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".