Toward autonomous additive manufacturing: Bayesian optimization on a 3D printer

摘要

Materials exploration and development for three-dimensional (3D) printing technologies is slow and labor-intensive. Each 3D printing material developed requires unique print parameters be learned for successful part fabrication, and sub-optimal settings often result in defects or fabrication failure. To address this, we developed the Additive Manufacturing Autonomous Research System (AM ARES). As a preliminary test, we tasked AM ARES with autonomously modulating four print parameters to direct-write single-layer print features that matched target specifications. AM ARES employed automated image analysis as closed-loop feedback to an online Bayesian optimizer and learned to print target features in fewer than 100 experiments. In due course, this first-of-its-kind research robot will be tasked with autonomous multi-dimensional optimization of print parameters to accelerate materials discovery and development in the field of AM. The combining of open-source ARES OS software with low-cost hardware makes autonomous AM highly accessible, promoting mainstream adoption and rapid technological advancement. Impact statement The discovery and development of new materials and processes for three-dimensional (3D) printing is hindered by slow and labor-intensive trial-and-error optimization processes. Coupled with a pervasive lack of feedback mechanisms in 3D printers, this has inhibited the advancement and adoption of additive manufacturing (AM) technologies as a mainstream manufacturing approach. To accelerate new materials development and streamline the print optimization process for AM, we have developed a low-cost and accessible research robot that employs online machine learning planners, together with our ARES OS software, which we will release to the community as open-source, to rapidly and effectively optimize the complex, high-dimensional parameter sets associated with 3D printing. In preliminary trials, the first-of-its-kind research robot, the Additive Manufacturing Autonomous Research System (AM ARES), learned to print single-layer material extrusion specimens that closely matched targeted feature specifications in under 100 iterations. Delegating repetitive and high-dimensional cognitive labor to research robots such as AM ARES frees researchers to focus on more creative, insightful, and fundamental scientific work and reduces the cost and time required to develop new AM materials and processes. The teaming of human and robot researchers begets a synergy that will exponentially propel technological progress in AM.