Radiopaque biodegradable polymers have been synthesized by ring-opening polymerization of L/DL-lactide and caprolactone with the iodine-containing starter molecule 2,2-bis(hydroxymethyl)propane-1,3-diyl bis(2,3,5-triiodobenzoate) followed by chain elongation with a diacid chloride or diisocyanates. The resulting polyesters and poly(ester-urethanes) exhibited a radiopacity of 60−124% relative to an aluminium sample of the same thickness. The polymers were processed into monofilament fibres by melt-spinning and into fibre meshes by electrospinning. All polymers were biodegradable in simulated body fluid medium under in vitro conditions and showed an excellent in vitro cytocompatibility even after several months of hydrolytic degradation. A current drawback is the relatively low tensile strength of the polymer monofilaments, which needs to be improved for applications as textile structures. Nevertheless, the new radiopaque and biodegradable polymers are promising candidates in fields of application where radiopacity of implants is an important parameter.

No abstract

Processes, such as deep rolling or induction hardening, have a remarkable influence on the material properties within the surface layer of a work piece. Our overall goal is to develop efficient two-scale methods, which are able to show the microstructural evolution of the machined material. The calculation of a spatially resolved microstructure comes along with a high computational effort. To reduce the computational costs, we combine a clustered description of the structure [1] with a model order reduction technique for the performed fast Fourier transformations (FFT) [2]. We choose a reduced set of Fourier modes, which is adapted to the underlying microstructure and thus based on the occurring strain field [3]. By that, we analyze the influence of a mechanical impact on an elasto-plastically deforming material.

Due to the general pursuit of technological advancement, structural components need to meet increasingly higher standards. In order to optimize the performance behavior of the used materials, detailed knowledge of the overall as well as microscopic material behavior under certain mechanical and thermal loading conditions is required. Hence, we present a two-scale finite element (FE) and fast Fourier transformation (FFT)-based method incorporating finite strains and a thermo-mechanically coupled constitutive model for elasto-viscoplastic polycrystalline materials. Assuming that the length scale of the microscale is sufficiently smaller compared to the length scale of the macroscale, we consider the macroscopic and microscopic boundary value problem as two coupled subproblems. The macroscopic boundary value problem is solved utilizing the finite element method. In each macroscopic integration point, the microscopic boundary value problem is embedded as a periodic unit cell whose solution fields are computed utilizing fast Fourier transforms and a Newton-Krylov solver. The scale transition is performed by defining the macroscopic quantities via the volume averages of their microscopic counterparts. In order to demonstrate the use of the proposed framework, we predict the macroscopic and microscopic fields of a polycrystalline material within a numerical example using an efficient and accurate FE-FFT-based two-scale method.

To capture all the individual microstructural effects of complex and heterogeneous materials in structural finite element simulations, a two‐scale simulation approach is necessary. Since the computational effort of such two‐scale simulations is extremely high, different methods exist to overcome this problem. In terms of a FFT‐based microscale simulation, one possibility is to use a reduced set of frequencies leading to a reduced numerical solution of the Lippmann‐Schwinger equation [?]. In a post‐processing step, highly resolved microstructural fields may then be reconstructed by using the compressed sensing technique [?]. Since the stress evaluation of this method is in real space and therefore not reduced, it is most beneficial in terms of linear elastic material behavior. Another very recent method to reduce the computational effort of a microscale simulation is the self‐consistent clustering analysis [?,?]. Such a self‐consistent clustering analysis is split into an offline and an online stage. Within the offline stage, the material points of the high‐fidelity representation of the unit cell are grouped into clusters with similar material behavior. Thereafter, in the online stage, a self‐consistent clustering analysis is used to solve the boundary value problem by a clustered Lippmann‐Schwinger equation. Since the generation of clusters may be based on linear elastic simulations, we propose to use a reduced set of frequencies for these simulations to improve the efficiency of the total algorithm. Elastic and elasto‐plastic composites are investigated in a small strain setting as representative simulation examples.