Autonomous Intuition for Advanced Manufacturing Systems: Benefits and Tradeoffs of Presampling for Autonomous Parameterization

Decades of progress in manufacturing automation have yielded technologies for highly customizable configurations of shape and materials. High‐mix, low‐volume methods, like additive manufacturing, require significant process optimization to utilize new materials or achieve certain complex geometric targets. Uncovering feasible parameters can require costly trial‐and‐error testing or artificially limiting the design process space. During parameter optimization, intuition often guides professionals who perform experiments, troubleshoot, or decide when to declare an effort untenable. With modern data‐driven tools, previously generated data can be used to create a model representation of intuition that may be used to guide further optimization. This work explores this concept through a case study of direct ink write 3D printing. By presampling a design space of freestanding geometries, we investigate the ability of a system to autonomously optimize various shapes within a target geometric class. The tradeoffs and benefits of presampling with traditional design of experiments and a targeted active learning scheme are examined in comparison to optimizing from no initial data. Leveraging a presampling scheme in broader open‐ended parameterization problems has the potential to accelerate adapting to new materials, enable autonomous systems to self‐define capabilities, and improve development time of future manufacturing methodologies.