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Title: A Bayesian experimental autonomous researcher for mechanical design
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 parametric family of structures and observe an almost 60-fold reduction in the number of experiments needed to identify high-performing structures relative to a grid-based search. These results show the value of machine learning in experimental fields where data are sparse.  more » « less
Award ID(s):
1661412
NSF-PAR ID:
10186120
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
Science Advances
Volume:
6
Issue:
15
ISSN:
2375-2548
Page Range / eLocation ID:
eaaz1708
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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