Additive manufacturing, also commonly referred to as 3D printing, stands to transform sand casting with binder jetting technology that can create sand molds with unmatched geometric complexity. With printed sand molds, castings can be optimized with regard to the strength-versus-weight trade-off and structures such as periodic lattices are now available within molds that are not possible with traditional casting technology. However, an increase in design complexity invites more challenges in terms of understanding and managing both the thermodynamics and physics of the casting process. Simulations of castings are more important than ever, and empirical in situ sensor data are required to validate high fidelity computer modeling (e.g., MAGMASOFT^®). One novel solution is to leverage the design freedom of CAD-based solid modeling to introduce unique mold features specifically for housing sensors (Internet of Things) within the mold to enable the collection of a diversity of data at manifold locations: temperature, pressure, moisture, gas chemistries, motion of the molds and internal cores (shifting or rotation), and magnetic field. This report describes a proof of concept in which unprecedented levels of process monitoring were integrated—both wirelessly and wired—at strategic locations throughout a printed mold and inside of internal cores. The collected data were used to validate the quality of a casting in situ as well as to provide feedback for optimizing the casting process, mold design, and simulations. A trade-off was explored between sensor survivability and disposability.

Three-dimensional porous scaffolds are substitutes for traditional bone grafts in bone tissue engineering (BTE) applications to restore and treat bone injuries and defects. The use of computational modelling is gaining momentum to predict the parameters involved in tissue healing and cell seeding procedures in perfusion bioreactors to reach the final goal of optimal bone tissue growth. Computational modelling based on finite element method (FEM) and computational fluid dynamics (CFD) are two standard methodologies utilised to investigate the equivalent mechanical properties of tissue scaffolds, as well as the flow characteristics inside the scaffolds, respectively. The success of a computational modelling simulation hinges on the selection of a relevant mathematical model with proper initial and boundary conditions. This review paper aims to provide insights to researchers regarding the selection of appropriate finite element (FE) models for different materials and CFD models for different flow regimes inside perfusion bioreactors. Thus, these FEM/CFD computational models may help to create efficient designs of scaffolds by predicting their structural properties and their haemodynamic responses prior to in vitro and in vivo tissue engineering (TE) applications.

This work covers different aspects of additive manufacturing of Nitinol parts. Firstly, requirements for the powder material and guidelines for the powder preparation are described in detail because the use of proper powder is essential for additive processing of high quality parts. Next, this work presents examples for Nitinol actuators, smart structures and devices which are produced by additive manufacturing. By demonstrating the functionality of these parts (e.g. shape recovery behavior after deformation), this work clearly points out a high potential for additive manufacturing of Nitinol. Moreover, additive manufacturing might even be able to open up new perspectives for Nitinol devices that have yet to be imagined.

Due to their excellent properties, triply periodic minimal surfaces (TPMS) have been applied to design scaffolds for bone tissue engineering applications. Predicting the mechanical response of bone scaffolds in different loading conditions is vital to designing scaffolds. The optimal mechanical properties can be achieved by tuning their geometrical parameters to mimic the mechanical properties of natural bone. In this study, we designed gyroid scaffolds of different user-specific pore and strut sizes using a combined TPMS and signed distance field (SDF) method to obtain varying architecture and porosities. The designed scaffolds were converted to various meshes such as surface, volume, and finite element (FE) volume meshes to create FE models with different boundary and loading conditions. The designed scaffolds under compressive loading were numerically evaluated using a finite element method (FEM) to predict and compare effective elastic moduli. The effective elastic moduli range from 0.05 GPa to 1.93 GPa was predicted for scaffolds of different architectures comparable to human trabecular bone. The results assert that the optimal mechanical properties of the scaffolds can be achieved by tuning their design and morphological parameters to match the mechanical properties of human bone.

Designing scaffolds similar to the structure of trabecular bone requires specialised algorithms. Existing scaffold designs for bone tissue engineering have repeated patterns that do not replicate the random stochastic porous structure of the internal architecture of bones. In this research, the Voronoi tessellation method is applied to create random porous biomimetic structures. A volume mesh created from the shape of a Zygoma fracture acts as a boundary for the generation of random seed points by point spacing to create Voronoi cells and Voronoi diagrams. The Voronoi lattices were obtained by adding strut thickness to the Voronoi diagrams. Gradient Voronoi scaffolds of pore sizes (19.8 µm to 923 µm) similar to the structure of the trabecular bone were designed. A Finite Element Method-based computational fluid dynamics (CFD) simulation was performed on all designed Voronoi scaffolds to predict the pressure drops and permeability of non-Newtonian blood flow behaviour using the power law material model. The predicted permeability (0.33 × 10⁻⁹ m² to 2.17 × 10⁻⁹ m²) values of the Voronoi scaffolds from the CFD simulation are comparable with the permeability of scaffolds and bone specimens from other research works.