Optimization of Process Parameters for Biological 3D Printing Forming Based on BP Neural Network and Genetic Algorithm

摘要

As one of rapid prototyping technologies, biological 3D printing forming process is used to prepare three-dimensional scaffolds for tissue engineering. Its complexity and unstability of its processing environment make it difficult to form three dimensional internal pore structure of bone scaffold. Thus, it is necessary to optimize the process parameters. In this paper, the orthogonal experiment is employed as Back Propagation(BP) neural network training sample to establish the nonlinear relationship between bone scaffold wire width and process parameters, then by optimizing the process parameters by Genetic Algorithm (GA), the optimal combination of the biological 3D printing forming process parameters is obtained. The forming experiment of bone scaffold's results show that based on BP neural network and Genetic Algorithm (GA), biological 3D printing forming process parameters optimization method is feasible and can help to get good quality bone scaffold.