A Bayesian experimental autonomous researcher for mechanical design.

ABSTRACT: While additive manufacturing (AM) has facilitated the production of complex structures, it has also highlighted the immense challenge inherent in identifying the optimum AM structure for a given application. Numerical methods are important tools for optimization, but experiment remains the gold standard for studying nonlinear, but critical, mechanical properties such as toughness. To address the vastness of AM design space and the need for experiment, we develop a Bayesian experimental autonomous researcher (BEAR) that combines Bayesian optimization and high-throughput automated experimentation. In addition to rapidly performing experiments, the BEAR leverages iterative experimentation by selecting experiments based on all available results. Using the BEAR, we explore the toughness of a pa

SUBMITTER: Gongora AE 

PROVIDER: S-EPMC7148087 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature