Prediction of tensile performance for 3D printed photopolymer gyroid lattices using structural porosity, base material properties, and machine learning

•The ability of a simple machine learning model to predict mechanical properties of 3D printed lattices is demonstrated.•The effect of printing defects on photopolymer lattice sample mechanical and geometric properties is highlighted.•A framework is presented for including both structure and material info into property predictions of 3D printed lattices.•Kernel ridge regression is used to make accurate mechanical property predictions using a small amount of training data.