SOURCE-LINKED INTELLIGENCE
Fatigue life assessment of additively manufactured material through a hybrid framework
ers. FLAME will address the aforementioned needs and pave the way towards efficient fatigue life assessment of AM via developing a hybrid framework through the association of physics-based models and machine learning approaches. It will improve the state-of-the-art fatigue life prediction accuracy from 75% to 90%, making them more economical by eliminating experiments and fast with an implementation time of less than 1 minute. FLAME, through its aforementioned performance, will effectively maximize the fatigue life at the design stage which results in a reduction of post-processing demand and a decrease in production costs. Moreover, it boosts the advancement of AM technology to replace the conventional methods paving the path to sustainable manufacturing and minimizing environmental impacts. Additive Manufacturing, Fatigue, Surface Integrity, Simulation
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- recordType
- award
- status
- SIGNED
- region
- EU
- value
- 188590.08
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T03:21:21.440Z. This is not the publication date.