Integrating machine learning and finite element simulation for interpretable prediction of 3D-printed bone scaffold mechanics

Abstract

An integrated framework combining finite element analysis (FEA) and artificial neural networks (ANN) is presented to enhance the prediction and design of bioprinted scaffolds. By leveraging the strengths of data-driven learning and physics-based simulations, the hybrid approach (ANN + FEA) achieved superior predictive accuracy and generalization compared to standalone approaches. Validation against experimental results demonstrated that a single ANN model yields a relative error of 5.17% when predicting the scaffold’s Young’s modulus. Incorporating FEA simulation based on ANN-predicted geometry and material properties reduced the relative error to 4.72%, representing an 8.6% improvement. The framework also enables accurate simulation of unseen combinations of printing parameters located far from the experimental data manifold, reducing prediction errors from 14.2% (ANNonly) to 5.7% (hybrid). By integrating predictive modeling, simulation, and data augmentation, this approach offers an efficient pathway for optimizing scaffold designs and accelerating the development of biomaterials with tailored mechanical performance.