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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Ambati, M.
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Publications (3/3 displayed)
- 2022Experimental-numerical analysis of microstructure-property linkages for additively manufactured materials
- 2021Accessing pore microstructure–property relationships for additively manufactured materialscitations
- 2019Phase-field modeling of brittle fracture with multi-level hp-FEM and the finite cell methodcitations
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article
Experimental-numerical analysis of microstructure-property linkages for additively manufactured materials
Abstract
The innovation of new or improved products fabricated from additive manufacturing processes with desired properties depends on a multitude of trials as stated by the National Science and Technology Council (2011). Therefore, a systematic approach is essential to accelerate materials development. This can be realised by developing systematic materials knowledge in the form of process-structure-property relationships. In this envisioned framework, the present work aims to derive the structure-property linkages of additively manufactured Ti-6Al-4V alloy. The main focus is to investigate the influence of potential defects, in the form of pores, inherited from the fabrication process on the fatigue properties. The complicated polycrystalline microstructure, including porosity at a microscale, is obtained by processing light microscopy and x-ray computed tomography measurements. A detailed statistical analysis is performed to obtain a low-dimensional representation of the structure. Based on these statistical measures, a suitable reconstruction algorithm is developed to create pore distributions that are incorporated into synthetic statistical volume elements (SVEs) generated from DREAM.3D by Groeber and Jackson (2014). Using these SVEs, microscale crystal plasticity simulations in DAMASK, see Roters et al. (2019), are performed to obtain the material properties such as yield strength and fatigue indicator parameters (FIPs). A detailed numerical analysis is carried out to study the influence of pore statistics such as size distribution or porosity fraction. Data analysis is carried out to rank-order the SVEs based on FIPs. Furthermore, a comparison with Murakami’s empirical square root area concept is made.