Reliability-based structural optimization with fatigue crack initiation constraint
Bibliographic record
Abstract
To implement the proposed probabilistic optimization method, two numerical examples are considered: the Cantilever Beam and the Scratch Drive Actuator (SDA), which is widely used in micro electromechanical systems (MEMS). Simulation results show that using the proposed optimization methodology significantly improves the accuracy of calculation. These results show that the difference between FOSM and FORM is growing by increasing the prescribed reliability for design. Furthermore, it is confirmed that the strain based fatigue approach is a more accurate design optimization criterion, in which both high cycle and low cycle fatigue life requirements, along with the local elastic-plastic analysis, are considered. The simulation results are also verified by Monte Carlo method or by existing data from the literature. This thesis presents a probabilistic optimization methodology for reliable design of structures with fatigue crack initiation constraint. A commonly used design optimization methodology for engineering systems comprises deterministic modeling. However, the existence of uncertainties in physical quantities, such as manufacturing tolerances, material properties, applied loads and fatigue life cycles, requires the probabilistic optimization technique. Moreover, very little research has been conducted to incorporate the fatigue life constraint into design optimization. To overcome this limitation in optimization techniques, a new reliability-based design optimization is proposed considering fatigue strength or fatigue life as a requirement in performance function. In this method, a multivariable objective function is constructed along with nonlinear probabilistic constraints. The probabilistic constraints consist of probability failure as well as fatigue failure criteria. The corresponding fatigue criterion is defined as the crack initiation phase in both stress- and strain-based models, respectively. The elements required for the probabilistic fatigue life calculations are also addressed. The first-order second-moment method (FOSM), first-order reliability method (FORM), and Monte Carlo method are adopted to assess the probability failure based on the concept of reliability indices. This concept is used to approximate the proposed fatigue life performance function about the most probable point. The sequential quadratic optimization technique is employed and a code is developed to solve the nonlinear optimization problem. The illustrative examples explain that the developed probabilistic optimization is not only more accurate and efficient, but it is also more realistic than the deterministic approach. Since reliability and fatigue failure are two important integrated parts in MEMS design, this proposed method can be applied in design optimization of these devices. This kind of methodology in design optimization is very practical and beneficial to industries, particularly in MEMS.
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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 | 0.000 | 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".