Study on the Energy Absorption Performance of Triply Periodic Minimal Surface (TPMS) Structures at Different Load-Bearing Angles.

As engineering demands for structural energy absorption intensify, triply periodic minimal surface (TPMS) structures, known for their light weight and exceptional energy absorption, are increasingly valued in aerospace, automotive, and shipping engineering. In this study, the energy absorption performance of three typical TPMS structures was evaluated (i.e., Gyroid, Diamond, and IWP) using quasi-static compression tests at various load-bearing angles. The results showed that while there is little influence of load-bearing angles on the energy absorption performance of Gyroid structures, its energy absorption is the least of the three structures. In contrast, Diamond structures have notable fluctuation in energy absorption at certain angles. Moreover, IWP (I-graph and Wrapped Package-graph) structures, though highly angle-sensitive, achieve the highest energy absorption. Further analysis of deformation behaviors revealed that structures dominated by bending deformation are stable under multi-directional loads but less efficient in energy absorption. Conversely, structures exhibiting mainly tensile deformation, despite their load direction sensitivity, perform best in energy absorption. By integrating bending and tensile deformations, energy absorption was enhanced through a multi-stage platform response. The data and conclusions revealed in the present study can provide valuable insights for future applications of TPMS structures.

Designing anisotropic porous bone scaffolds using a self-learning convolutional neural network model.

The design of bionic bone scaffolds to mimic the behaviors of native bone tissue is crucial in clinical application, but such design is very challenging due to the complex behaviors of native bone tissues. In the present study, bionic bone scaffolds with the anisotropic mechanical properties similar to those of native bone tissues were successfully designed using a novel self-learning convolutional neural network (CNN) framework. The anisotropic mechanical property of bone was first calculated from the CT images of bone tissues. The CNN model constructed was trained and validated using the predictions from the heterogonous finite element (FE) models. The CNN model was then used to design the scaffold with the elasticity matrix matched to that of the replaced bone tissues. For the comparison, the bone scaffold was also designed using the conventional method. The results showed that the mechanical properties of scaffolds designed using the CNN model are closer to those of native bone tissues. In conclusion, the self-learning CNN framework can be used to design the anisotropic bone scaffolds and has a great potential in the clinical application.

A Critical Review on the Design, Manufacturing and Assessment of the Bone Scaffold for Large Bone Defects.

In recent years, bone tissue engineering has emerged as a promising solution for large bone defects. Additionally, the emergence and development of the smart metamaterial, the advanced optimization algorithm, the advanced manufacturing technique, etc. have largely changed the way how the bone scaffold is designed, manufactured and assessed. Therefore, the aim of the present study was to give an up-to-date review on the design, manufacturing and assessment of the bone scaffold for large bone defects. The following parts are thoroughly reviewed: 1) the design of the microstructure of the bone scaffold, 2) the application of the metamaterial in the design of bone scaffold, 3) the optimization of the microstructure of the bone scaffold, 4) the advanced manufacturing of the bone scaffold, 5) the techniques for assessing the performance of bone scaffolds.