Numerical and Experimental Optimization for Specific Fatigue Life Maximization of Additively Manufactured Ti-6Al-4V Aerospace Components
Bibliographic record
Abstract
Computer Aided Design (CAD) in combination with structural optimization has allowed the rapid development of engineering prototypes.Topology and multi-scale design optimization techniques are capable of shaping highly efficient load paths.However, the resulting geometries are complex that they can only be manufactured with Additive Manufacturing (AM) technology.One of the main downfalls of AM parts is their poor fatigue life which hinders the potential merits of design optimization.This thesis investigates experimental and numerical strategies to extend the fatigue life of additively manufactured Ti-6Al-4V components manufactured by Direct Metal Laser Sintering (DMLS) technology.Three stages for the development of fatigue resistant AM parts are introduced.The first experimental stage is concerned with the application of Ultrasonic Impact Treatment (UIT) to enhance the fatigue life of AM parts and to derive the S-N curves for the fatigue behavior of the treated and the as built parts.The second numerical stage is to conduct design optimization employing multi-scale, multi-objective and topology optimization methods to maximize the specific fatigue life of mechanical components made of UIT treated Ti-6Al-4V.A third stage includes employing cellular material in the context of topology and multi-scale optimization to further minimize the weight and maximize the fatigue life in a multi-objective design optimization scheme.It is found that by applying UIT onto DMLS Ti-6Al-4V that the endurance limit increases by 25%.When UIT is applied in the context of specific fatigue maximization, then a 51% mass reduction can be achieved while maintaining infinite life criterion.
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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 | 0.001 |
| 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) | 0.001 | 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".