A review about design, manufacture, and mechanobiology of biodegradable scaffolds for bone tissue engineering is given. First, fundamental aspects about bone tissue engineering and considerations related to scaffold design are established. Second, issues related to scaffold biomaterials and manufacturing processes are discussed. Finally, mechanobiology of bone tissue and computational models developed for simulating how bone healing occurs inside a scaffold are described.

Scaffolds are essential in bone tissue engineering, as they provide support to cells and growth factors necessary to regenerate tissue. In addition, they meet the mechanical function of the bone while it regenerates. Currently, the multiple methods for designing and manufacturing scaffolds are based on regular structures from a unit cell that repeats in a given domain. However, these methods do not resemble the actual structure of the trabecular bone which may work against osseous tissue regeneration. To explore the design of porous structures with similar mechanical properties to native bone, a geometric generation scheme from a reaction-diffusion model and its manufacturing via a material jetting system is proposed. This article presents the methodology used, the geometric characteristics and the modulus of elasticity of the scaffolds designed and manufactured. The method proposed shows its potential to generate structures that allow to control the basic scaffold properties for bone tissue engineering such as the width of the channels and porosity. The mechanical properties of our scaffolds are similar to trabecular tissue present in vertebrae and tibia bones. Tests on the manufactured scaffolds show that it is necessary to consider the orientation of the object relative to the printing system because the channel geometry, mechanical properties and roughness are heavily influenced by the position of the surface analyzed with respect to the printing axis. A possible line for future work may be the establishment of a set of guidelines to consider the effects of manufacturing processes in designing stages.Highlights We model scaffolds structures for bone tissue engineering using a reaction-diffusion system. Geometrical properties such as channel width can be adjusted using this methodology. Mechanical and geometrical features of parts made using a material jetting system are described.

The wall thickness and build direction of PA 12 parts manufactured via SLS are currently being investigated for applications requiring low weight and good mechanical properties (ultimate tensile strength, elongation at break, elastic modulus, and hardness). Current design guidelines for the SLS process include recommendations about the influence of build direction on the mechanical properties of the part; however, the recommended minimum wall thickness only considers the process's manufacturability, so this study aims to determine the wall thickness and build orientation conditions that present the slightest difference in mechanical properties, considering different conditions as vertical, horizontal, and transverse build directions and wall thicknesses of 2.0, 2.5 and 3.0 mm. Statistical differences were found between build direction, ultimate tensile stress, and elongation at break and hardness. It was observed that the significant differences in the direction of impression were between the vertical direction versus the transverse and horizontal directions. The differences were between the values of 2.0 mm versus 2.5 and 3.0 mm for the wall thickness. The mechanical properties between these last two thicknesses do not present significant differences, so it is suggested that parts manufactured with PA 12 by SLS with thicknesses of 2.5 mm could have the same tensile mechanical properties as those of 3.0 mm. The horizontal and transverse directions with a 2.5–3.0 mm thickness showed the highest mechanical properties with an ultimate tensile stress of ≈ 43 MPa, a modulus of elasticity of 2.2 GPa, and an elongation at break of 16%–18% and a hardness of ≈ 75 Shore D.

Machine learning techniques are increasingly employed to predict material properties and reduce mechanical testing costs. This study uses tensile load‒displacement data to predict the breaking force, maximum force, ultimate tensile strength (UTS), and breaking strength of nylon PA 12 specimens manufactured via selective laser sintering (SLS). To address variations in specimen ductility, linear and fourth-degree polynomial equalisations were applied to the data. The results indicate that polynomial equalisation generally achieves superior predictive performance across various machine learning models. While all the models exhibit a decline in performance metrics with a reduction in the observation matrix data, several demonstrate strong predictive capabilities even with reduced data sets. Specifically, when using 20% of the observation matrix data (corresponding to the elastic zone) are used, the kernel ridge, elastic net, and Lasso regression models achieve R ² values of 0.987, 0.931, and 0.987, respectively, for predicting breaking strength. For maximum force prediction with 20% of the data, the kernel ridge, support vector machine, and Lasso regression models resulted in R ² values of 0.975, 0.960, and 0.930, respectively. However, the prediction accuracy for UTS and ultimate strength at break is lower, with the Elastic Net model achieving R ² values of 0.703 and 0.682, respectively, with 20% of the data. These findings suggest that while certain mechanical properties can be accurately predicted using data solely from the elastic zone, minimising specimen waste and testing time, the prediction of other properties requires data beyond the elastic limit. This study highlights the potential of machine learning to optimise and streamline the quality control process in additive manufacturing.