Predicting fatigue life of additively manufactured lattice structures using the image-based Finite Cell Method and average strain energy density
A well-known Achilles' heel of laser powder bed fusion (L-PBF) additive manufactured lattice structures is the difficulty in predicting fatigue properties. The presence of manufacturing-induced defects significantly affects the fatigue resistance of the porous component and must be accurately captured by predictive models. To tackle this challenge, the as-built geometry of the lattice needs to be modeled, which introduces another challenge on the computational front. For the first time, a model based on the computer tomography ( μ -CT) reconstruction of the as-built lattice geometry is simulated with the efficient finite cell method and combined with the average strain energy density (ASED) to obtain accurate fatigue predictions. This work presents a workflow for determining the fatigue resistance of lattice metamaterial, followed by a case study for method validation. The validation shows a good agreement between the predicted fatigue life and the experimental results. • Fatigue life prediction on as-built geometries obtained from μ -CT analysis. • Finite cell method is used to simulate computationally challenging models. • Average strain energy density approach is used to predict fatigue life Highlights.
Authors
- Filippo Zanini (ORCID: https://orcid.org/0000-0002-4465-1230)
- Oguz Oztoprak (ORCID: https://orcid.org/0000-0002-5326-6721)
- M. Benedetti
- Simone Carmignato (ORCID: https://orcid.org/0000-0001-6135-6834)
- Raffaele De Biasi (ORCID: https://orcid.org/0000-0002-3755-1573)
- Stefan Kollmannsberger (ORCID: https://orcid.org/0000-0003-0823-8649)
Institutions
- University of Padua (IT)
- University of Trento (IT)
- Bauhaus-Universität Weimar (DE)
Publication Details
- Journal
- Materials & Design
- Published
- 2024-09-19
- DOI
- https://doi.org/10.1016/j.matdes.2024.113321
- Citations
- 13
- Primary Topic
- Additive Manufacturing Materials and Processes
- Type
- article
- Field-Weighted Citation Impact
- 2.76
Funders
- Deutsche Forschungsgemeinschaft
- Università degli Studi di Trento