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 |
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| creator | Wang, Yingjun Liao, Zhongyuan Shi, Shengyu Wang, Zhenpei Poh, Leong Hien |
| description | 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 variables, the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method, and demonstrated in this work to give high accuracy and efficiency. Such BPNN-based fitting functions also enable
an easy analytical sensitivity analysis, in contrast to the generally complex procedures of typical shape and size sensitivity approaches. |
| doi_str_mv | 10.32604/cmes.2020.08680 |
| format | Article |
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number of design variables, the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method, and demonstrated in this work to give high accuracy and efficiency. Such BPNN-based fitting functions also enable
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number of design variables, the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method, and demonstrated in this work to give high accuracy and efficiency. Such BPNN-based fitting functions also enable
an easy analytical sensitivity analysis, in contrast to the generally complex procedures of typical shape and size sensitivity approaches.</abstract><cop>Henderson</cop><pub>Tech Science Press</pub><doi>10.32604/cmes.2020.08680</doi><tpages>26</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | 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 |
| title | Data-Driven Structural Design Optimization for Petal-Shaped Auxetics Using Isogeometric Analysis |
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