1. Design optimization of bio-inspired 3D printing by machine learning.
- Author
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Goto, Daiki, Matsuzaki, Ryosuke, and Todoroki, Akira
- Subjects
ARTIFICIAL neural networks ,THREE-dimensional printing ,MATHEMATICAL optimization ,STIFFNERS ,MACHINE learning ,QUADRATIC programming - Abstract
In this study, the stiffener geometry was optimized using curvilinear 3D printing to enhance the buckling resistance. A bio-inspired skin/stiffener composite that mimicked spider-web structures was generated. A dataset was formulated for the regression analysis, covering buckling stresses under distinct feature values. The regression equations, crafted using a deep neural network trained on the dataset, were evaluated. The derived regression equation was subjected to sequential quadratic programming, a mathematical optimization, to determine the optimal value of the explanatory variable. This was aimed at maximizing the buckling stress-to-stiffener volume ratio, which is the objective variable. The optimized arrangement exhibited significantly improved buckling resistance, with approximately 163% higher buckling stress than conventionally designed structures with straight stiffeners of similar weight. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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