Design for Interface Stiffness of Mechanical Products Using Integrated Simulation and Optimization Under Uncertainty
Bibliographic record
Abstract
Abstract Interface stiffness is an important factor influencing the performance of mechanical products. Uncertain factors affect the interface stiffness and stability in the process of product design, manufacture, and operation. How to reduce the impact of uncertain factors on the interface stiffness is a vital problem in interface design. In this paper, a robust optimal design method is proposed for mechanical interfaces considering uncertain factors, which combines the finite element simulation, experiment, and optimization to reduce the sensitivity of interface stiffness to uncertain factors. The proposed interface design method provides an effective way to improve the interface stiffness under uncertain conditions. In order to validate the proposed method, the bolted connection structure of a flange is applied as an example. The interface stiffness of the flange is selected as an optimization target, and the Gaussian process regression is used to construct a two-layer optimal model of the objective function for the design and uncertain parameters. When experimental and optimization results differ significantly, the Kalman filter is used to provide the feedback for the optimization results until the results meet requirements. The final results show that the optimized mechanical interface stiffness is increased by 15.5%, and the error between the optimized prediction and experimental results is within 1% after three times experimental validation and feedback adjustment.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".