Richert, ClaudiaClaudiaRichertOdermatt, AntonAntonOdermattHuber, NorbertNorbertHuber2020-01-152020-01-152019-12-18Frontiers in Materials (6): 327 (2019-12-18)http://hdl.handle.net/11420/4364Identifying local thickness information of fibrous or highly porous structures is challenging. The analysis of tomography data calls for computationally fast, robust, and accurate algorithms. This work systematically investigates systematic errors in the thickness computation and the impact of observed deviations on the predicted mechanical properties using a set of 16 model structures with varying ligament shape and solid fraction. Strongly concave, cylindrical, and convex shaped ligaments organized in a diamond structure are analyzed. The predicted macroscopic mechanical properties represent a highly sensitive measure for systematic errors in the computed geometry. Therefore, the quality of proposed correction methods is assessed via FEM beam models that can be automatically generated from the measured data and allow an efficient prediction of the mechanical properties. The results show that low voxel resolutions can lead to an overprediction of up to 30% in the Young's modulus. A model scanned with a resolution of 200 voxels per unit cell edge (8M voxels) reaches an accuracy of a few percent. Analyzing models of this resolution with the Euclidean distance transformation showed an underprediction of up to 20% for highly concave shapes whereas cylindrical and slightly convex shapes are determined at high accuracy. For the Thickness algorithm, the Young's modulus and yield strength are overpredicted by up to 100% for highly concave ligament shapes. A proposed Smallest Ellipse approach corrects the Thickness data and reduces this error to 20%. It can be used as input for a further robust correction of the Thickness data using an artificial neural network. This approach is highly accurate with remnant errors in the predicted mechanical properties of only a few percent. Furthermore, the data from the FEM beam models are compared to results from FEM solid models providing deeper insights toward further developments on nodal corrections for FEM beam models. As expected, the FEM beam models show an increasing overprediction of the compliance with increasing solid fraction. As an unexpected result, the mechanical strength can however be underpredicted or overpredicted, depending on the ligament shape. Therefore, a nodal correction is needed that solves contradicting tasks in terms of stiffness and strength.en2296-80162019Frontiers Mediahttps://creativecommons.org/licenses/by/4.0/artificial neural networkFEM beam modelfoamsnanoporous goldskeletonizationthickness correctiontomographytrabecular boneTechnikJournal Article10.15480/882.257010.3389/fmats.2019.0032710.15480/882.2570Other