journal article·2018-08Open access

Multiscale modeling of fatigue crack propagation in additively manufactured porous biomaterials

R. Hedayati, H. Hosseini-Toudeshky, M. Sadighi, M. Mohammadi-Aghdam, A.A. Zadpoor

Abstract

Abstract Advances in additive manufacturing (AM) techniques have enabled fabrication of highly porous titanium implants that combine the excellent biocompatibility of bulk titanium with all the benefits that a regular volume-porous structure has to offer (e.g. lower stiffness values comparable to those of bone). Clinical application of such biomaterials requires thorough understanding of their mechanical behavior under loading. Computational models have been therefore developed by various groups for prediction of their quasi-static mechanical properties. The fatigue behavior of AM porous biomaterials is, however, not well understood. In particular, computational models predicting the fatigue response of these structures are rare. That is primarily due to the fact that geometrical features present in computational model of fully porous structures span over multiple length scales. This makes the problem formidably expensive to solve computationally. Here, we propose a multi-scale modeling approach to alleviate this problem and solve the problem of crack propagation in AM porous biomaterials. In this approach, the area around the crack tip is modelled at the micro-scale (using beam elements) while the area far from the crack tip is modeled at the macro-scale (using volumetric elements). Compact-tension notched specimens were fabricated using a selective laser melting machine for validating the results of the presented modeling approach. The multi-scale computational model was found to be capable of predicting the fatigue response observed in experiments.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Additive Manufacturing Materials and Processes · Additive Manufacturing and 3D Printing Technologies · Titanium Alloys Microstructure and Properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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