Preferential Bayesian optimization improves the efficiency of printing objects with subjective qualities

Abstract

Despite recent advances in closed-loop 3D printing, optimizing subjective and difficult-to-quantify qualities—such as surface finish and clarity of fine detail—remains a significant challenge, often relying on the traditional time-consuming and inefficient trial-and-error process. Preferential Bayesian optimization (PBO) is a machine learning technique that uses human preference judgements to efficiently guide the search for such abstract optimums in a high-dimensional space. We evaluated PBO's ability to identify optimal parameter values in printing profiles of vases and pairs of 3D cones. In semi-autonomous printing campaigns, a human observer ranked triplets of images of these objects with a target object in mind, preferring slender/bulbous vases and cone pairs that were smooth and well-formed. Results show that PBO consistently and quickly identified an optimal parameter combination across repeated testing. Modeling was then used to identify object dimensions responsible for preference judgements and to mimic preference behavior. Findings suggest that PBO is a promising tool for expanding the range of 3D objects that can be printed efficiently.