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Multi-objective Bayesian optimization: a case study in material extrusion
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DOI：10.1039/d4dd00281d.png)
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Abstract
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Autonomous experimentation is a rapidly growing approach to materials science research. Machine learning can assist in improving the efficiency and capability of experimentation with algorithms that adaptively identify optimal design parameters that achieve one or more objectives in iterative, closed-loop fashion. Optimization in additive manufacturing, which can be slow and costly because of its complexity, stands to benefit greatly from such technologies. The present study demonstrates the application of an algorithm (multi-objective Bayesian optimization; MOBO) that optimizes two objectives simultaneously given multiple parameter inputs. The generality and robustness of MOBO are demonstrated in repeated print campaigns of two different test specimens. The results push the boundaries of integrating machine learning with autonomous experimentation for accelerated materials development in additive manufacturing and related areas.
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PROCEEDINGS OF THE IEEEIF25.9
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