Machine Learning Enabled Design and Optimization for 3D-Printing of High-Fidelity Presurgical Organ Models

Abstract

The development of a general-purpose machine learning algorithm capable of quickly identifying optimal 3D-printing settings can save manufacturing time and cost, reduce labor intensity, and improve the quality of 3D-printed objects. Existing methods have limitations which focus on overall performance or one specific aspect of 3D-printing quality. Here, for addressing the limitations, a multi-objective Bayesian Optimization (BO) approach which uses a general-purpose algorithm to optimize the black-box functions is demonstrated and identifies the optimal input parameters of direct ink writing for 3D-printing different presurgical organ models with intricate geometry. The BO approach enhances the 3D-printing efficiency to achieve the best possible printed object quality while simultaneously addressing the inherent trade-offs from the process of pursuing ideal outcomes relevant to requirements from practitioners. The BO approach also enables us to effectively explore 3D-printing inputs inclusive of layer height, nozzle travel speed, and dispensing pressure, as well as visualize the trade-offs between each set of 3D-printing inputs in terms of the output objectives which consist of time, porosity, and geometry precisions through the Pareto front.