Search

Porous implants composed of periodically-repeating porous units have been applied in orthopedic surgeries to replace damaged bones. An important requirement for bone implants is to find a balance between biological and mechanical properties, that is, to ensure high pore connectivity and a certain strength. In this work, center-supported and perimeter-supported porous optimization models were established by a swarm intelligence optimization algorithm, and the corresponding titanium porous samples were fabricated by laser powder bed fusion (L-PBF). The effects of cell topology and porosity on the compressive behavior of L-PBF titanium porous structures were systematically investigated by a combination of compression simulation and experiments. Compression simulation shows that the deformation failure behavior of center-supported and perimeter-supported porous structures consists of three stages: elastic deformation, plastic deformation and compression collapse, showing the characteristics of elastic-plastic porous materials. According to the compression test results, the compressive strength of center-supported and perimeter-supported structure samples are 151–188 MPa and 178–196 MPa, respectively, and the compressive stability of the perimeter-supported structure is better than that of the center-supported structure. The porosity error of center-supported structure is 1.6%–2.9%, and the porosity error of perimeter-supported structure is 3.1%–4.5%. The compressive strength of the same type of structure decreases with the increase of the porosity of the porous structure. The simulation/experimental compressive strength errors of center-supported and perimeter-supported structures are less than 6% and 5.9%, respectively. The constructed quasi-static compression model has certain guiding significance for the study of the fracture of the titanium implants under external force.

Excellent mechanical properties, including residual stress, ductility and strength, are important requirements for bone implants. In order to achieve this effect, titanium samples were manufactured by selective laser melting, and the prepared parts were subsequently subjected to three different heat treatment, which were 550 °C, 650 °C and 750 °C for 2 h, respectively. The results show that for as-built sample, relatively short lath-shaped α grains and a few needle-shaped α′ grains were present. After heat treatment, the α grains became thicker and longer, and the α′ grains became more. As the heat treatment temperature increases from 550 °C to 750 °C, the size of the lath-shaped α grains gradually decreases, and the small-sized needle-shaped α′ grains become denser. Moreover, the tensile strength, elongation and shrinkage of specimens increased as the heat treatment temperature increased. The fine microstructure of heat-treated samples also significantly promoted the improvement of tensile properties. The fracture mechanism of the specimens changed from brittle fracture to a mixed mode including ductile and brittle fracture, so that the tensile strength and ductility of the heat-treated specimens were well combined.

In order to achieve good adaptability, medical bone implants for clinical applications need to have porous characteristics. From a biological and mechanical point of view, the design of porous structures requires both suitable porosities to facilitate cell ingrowth and suitable strength to avoid implant damage. To handle the multiobjective optimization problems of porous structure design, this work introduced an improved multi-objective optimization algorithm, which is called a multi-swarm multi-objective evolutionary algorithm based on decomposition (MSMOEA/D), and the main idea is a multi-swarm strategy. After a predetermined algebraic evolution, the whole swarm was evenly divided into several parts, and the elite non-dominated sorting mechanism was used to select the individuals with excellent performance and poor performance in the sub-swarms to exchange information between the sub-swarms. The performance of the MSMOEA/D algorithm was verified and validated on 12 constraint two-objective and three-objective benchmark functions and compared with MOEA/D, MOEADM2M, and MOEADDRA algorithms in terms of generational distance indicators. The solutions obtained by the proposed MSMOEA/D algorithm were accurate. Finally, the proposed MSMOEA/D algorithm was applied to optimize the constructed RS porous structure, and the porous optimized models with porosities of 50%, 60% and 70% were obtained.