X-ray micro-computed tomography (micro-CT) is an advanced technique able to provide a comprehensive examination of the volumetric characteristics of asphalt mixtures. A key step for the air void (AV) quantification using micro-CT images is the segmentation, which is a stage of the digital image processing. The most common segmentation technique, the manual threshold (TH) selection, depends significantly on the operator skills, image homogeneity, and material complexity. These factors that can limit the reproducibility of the TH procedure. Machine learning and deep learning recently appeared as promising alternatives to solve this challenge. In this paper, images of an asphalt concrete (AC) specimen were acquired in a modern high-resolution micro-CT scanner to determine its AV content using four different segmentation tools, i.e. TH, watershed, machine learning, and deep learning. All methods presented similar results for the total AV content. The advantages and limitations of using each technique were discussed in terms of computational effort, user-friendliness, and accuracy of the results. Machine learning and deep learning were identified as powerful tools for AC segmentation, being accurate and easy to adjust, however taking longer data processing times.

Permeability is a parameter that measures the resistance that fluid faces when flowing through a porous medium. Usually, this parameter is determined in routine laboratory tests by applying Darcy’s law. Those tests can be complex and time-demanding, and they do not offer a deep understanding of the material internal microstructure. Currently, with the development of new computational technologies, it is possible to simulate fluid flow experiments in computational labs. Determining permeability with this strategy implies solving a homogenization problem, where the determination of the macro parameter relies on the simulation of a fluid flowing through channels created by connected pores present in the material’s internal microstructure. This is a powerful example of the application of fluid mechanics to solve important industrial problems (e.g., material characterization), in which the students can learn basic concepts of fluid flow while practicing the implementation of computer simulations. In addition, it gives the students a concrete opportunity to work with a problem that associates two different scales. In this work, we present an educational code to compute absolute permeability of heterogeneous materials. The program simulates a Stokes flow in the porous media modeled with periodic boundary conditions using finite elements. Lastly, the permeability of a real sample of sandstone, modeled by microcomputed tomography (micro-CT), is obtained.

Casein phosphopeptide-amorphous calcium phosphate (CPP-ACP) complexes are anticariogenic and capable of remineralizing the early stages of enamel lesions. The use of fluoride prevents dental decay and the association of CPP-ACP with fluoride can increase remineralization.

Herein, we describe in detail a methodology to estimate the effective elastic parameters of nodular cast iron, using micro-tomography in conjunction with multiscale finite elements. We discuss the adjustment of the image acquisition parameters, address the issue of the representative-volume choice, and present a brief discussion on image segmentation. In addition, the finite-element computational implementation developed to estimate the effective elastic parameters from segmented microstructural images is described, indicating the corresponding computational costs. We applied the proposed methodology to a nodular cast iron, and estimated the graphite elastic parameters through a comparison between the numerical and experimental results.