Accelerated discovery of 3D printing materials using data-driven multiobjective optimization.

ABSTRACT: Additive manufacturing has become one of the forefront technologies in fabrication, enabling products impossible to manufacture before. Although many materials exist for additive manufacturing, most suffer from performance trade-offs. Current materials are designed with inefficient human-driven intuition-based methods, leaving them short of optimal solutions. We propose a machine learning approach to accelerating the discovery of additive manufacturing materials with optimal trade-offs in mechanical performance. A multiobjective optimization algorithm automatically guides the experimental design by proposing how to mix primary formulations to create better performing materials. The algorithm is coupled with a semiautonomous fabrication platform to substantially reduce the number of perform

SUBMITTER: Erps T 

PROVIDER: S-EPMC8519564 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature