Multi-Objectives Optimization of Fastener Location in a Bolted Joint
Bibliographic record
Abstract
During component development of multiple fastener bolted joints, it was observed that one or two fasteners had a higher potential to slip when compared to other fasteners in the same joint. This condition indicated that uneven distribution of the service loads was occurring in the bolted joints. The need for an optimization tool was identified that would take into account various objectives and constraints based on real world design conditions. The objective of this paper is to present a method developed to determine optimized multiple fastener locations within a bolted joint for achieving evenly distributed loads across the fasteners during multiple load events. The method integrates finite element analysis (FEA) with optimization software using multi-objective optimization algorithms. Multiple constraints were also considered for the optimization analysis. In use, each bolted joint is subjected to multiple service load conditions (load cases). Each of these load cases requires evaluation to validate the fastened joint. Additionally, geometric limitations that define the available joint footprint had to be included in the analysis. The case study was conducted on 3-bolt and 4- bolt powertrain mount brackets. By using this multi-objective optimization tool, the fasteners' locations are determined based on equalizing the shear loads on each fastener for all the joint service load cases, and a robust joint design is achieved by identifying the minimum fastener size required to maintain joint integrity and fastener commonality.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".