Genetic algorithms and finite element coupling for mechanical optimization
Bibliographic record
Abstract
Optimization of mechanical components is an important aspect of the engineering process; a well-designed system will lead to money saving during the production phase and better machine life. On the other hand, optimization actions will increase the engineering investment. Consequently, and since computer time is inexpensive, an efficient design strategy will tend to transfer the effort from the staff to the computers. This paper presents an efficient design tool made to carry out this task: a new optimization model based on genetic algorithms is developed to work with commercial finite element software. The objective is to automate optimization of static criteria (stresses, weight, strength, etc.) with finite element models. In the proposed model, the process acts on two geometric aspects of the shape to be optimized: it controls the position of the vertices defining the edges of the volume and, in order to minimize stresses concentrations, it can add and define fillet between surfaces. The model is validated from some benchmark tests. An industrial application is presented: the genetic algorithms-finite element model is employed to design the fillets at the crown-blade junctions of a hydroelectric turbine. The results show that the model converges to a very efficient solution without any engineer intervention.
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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".