Data-Driven Structural Design Optimization for Petal-Shaped Auxetics Using Isogeometric Analysis

Focusing on the structural optimization of auxetic materials using data-driven methods, a back-propagation neural network (BPNN) based design framework is developed for petal-shaped auxetics using isogeometric analysis. Adopting a NURBS-based parametric modelling scheme with a small number of design...

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Veröffentlicht in:	Computer modeling in engineering & sciences 2020-01, Vol.122 (2), p.433-458
Hauptverfasser:	Wang, Yingjun, Liao, Zhongyuan, Shi, Shengyu, Wang, Zhenpei, Poh, Leong Hien
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Auxetic materials Bp Neural Network Data-Driven Design optimization ISOGEOMETRIC ANALYSIS Material properties Negative Poisson's Ratio Neural networks Petal-Shaped Auxetics Sensitivity analysis Structural Design
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