Stand-alone and portable lab-on-chips (LOC) can be obtained by exploiting capillary flow in porous media. Polymethylmethacrylate (PMMA) platforms obtained through powder-based three-dimensional (3D) printing are appropriate for capillarity-driven LOCs. However, fluid flow in such platforms needs to be characterized well. For this purpose, a 3D pore network (PN) was extracted from high-resolution $\mu \mathrm{CT}$ images of printed PMMA through a watershed algorithm and a PN model was developed with the final goal of characterizing material permeability. The effect of all parameters involved in the PN extraction and modeling was investigated. The study focused in particular on the effect of the number of seeds for the watershed segmentation, pore sphericity, and pore-to-pore channel shape that was modeled as a bicylindrical or biconical object. The results proved that all PN extraction and modeling parameters influenced the permeability, which was found to be lower the higher the number of seeds and when using sphericity and biconical channels. Eventually, the Calinski-Harabasz index value was used to identify the optimal number of watershed seeds.

Additive manufacturing or 3D printing can be applied in the food sector to create food products with personalized properties such as shape, texture, and composition. In this article, we introduce a computer aided engineering (CAE) methodology to design 3D printed food products with tunable mechanical properties. The focus was on the Young modulus as a proxy of texture. Finite element modelling was used to establish the relationship between the Young modulus of 3D printed cookies with a honeycomb structure and their structure parameters. Wall thickness, cell size, and overall porosity were found to influence the Young modulus of the cookies and were, therefore, identified as tunable design parameters. Next, in experimental tests, it was observed that geometry deformations arose during and after 3D printing, affecting cookie structure and texture. The 3D printed cookie porosity was found to be lower than the designed one, strongly influencing the Young modulus. After identifying the changes in porosity through X-ray micro-computed tomography, a good match was observed between computational and experimental Young’s modulus values. These results showed that changes in the geometry have to be quantified and considered to obtain a reliable prediction of the Young modulus of the 3D printed cookies.