期刊
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
卷 122, 期 2, 页码 433-458出版社
TECH SCIENCE PRESS
DOI: 10.32604/cmes.2020.08680
关键词
Data-driven; BP neural network; petal-shaped auxetics; negative Poisson's ratio; structural design; isogeometric analysis
资金
- National Natural Science Foundation of China [51705158, 51805174]
- Fundamental Research Funds for the Central Universities [2018MS45, 2019MS059]
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.
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