Prediction of the mechanical properties of TPMS structures based on Back propagation neural network

Abstract

Triply Periodic Minimal Surface (TPMS) has the characteristics of high porosity, a highly interconnected network, and a smooth surface, making it an ideal candidate for bone tissue engineering applications. However, due to the complex relationship between multiple parameters of the TPMS structure and mechanical properties, it is a challenging task to optimize the properties of TPMS structures with different parameters. In this study, a Back-Propagation Neural Network (BPNN) was utilized to construct the relationship between TPMS parameters. Its mechanical performance and the TPMS structure were optimized using the BPNN. Results indicated that after training the correlation coefficient (R) between the BPNN prediction and the experimental results is 0.955475, it shows that our BPNN model has an adequate accuracy in describing the TPMS structures properties. Result of TPMS structure optimization shows that after optimization the yield strength of Hybridized Gyroid-Diamond Structure (HGDS) is 6.20 MPa, which is increased by 102.61% when compared with the original Hybridized Gyroid-Diamond Structure (3.06 MPa). Result of topological morphology indicates the effective bearing area of the optimized model was increased by 12.92% compared with the original model, which ascribe the increase in yield strength of the optimization model.