Abstract Scope |
Additive manufacturing has enabled advancement in high-mix low-volume manufacturing and rapid prototyping of materials and geometries. Selecting process parameters for new materials, geometries, applications, or hardware configurations in additive systems currently relies on intuition-based trial and error. However, the difference between machines and humans limit the power of this intuition. Can machines develop their own “intuition” and how much data does such a process require? To answer these questions, we are developing an automated rapid parameterization protocol. Specifically, this work proposes a machine-learning-driven automated parameterization scheme demonstrated with the direct ink write (DIW) printing of freestanding structures. This case study examines several factors crucial to the success of automated parameterization including efficiency, design space considerations, use of prior data, and physical limitations. Ideally, automated training of machine intuition will enable more agile manufacturing, high-throughput screening of materials and geometries, and more efficient collaboration across systems. |