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

被引：48

作者：

Wang, Yingjun ^{[1
]}

Liao, Zhongyuan ^{[1
]}

Shi, Shengyu ^{[1
]}

Wang, Zhenpei ^{[2
]}

Poh, Leong Hien ^{[3
]}

机构：

[1] South China Univ Technol, Guangdong Prov Key Lab Tech & Equipment Macromol, Minist Educ, Key Lab Polymer Proc Engn,Natl Engn Res Ctr Novel, Guangzhou 510641, Guangdong, Peoples R China

[2] ASTAR, IHPC, 1 Fusionopolis Way, Singapore 138632, Singapore

[3] Natl Univ Singapore, Dept Civil & Environm Engn, 1 Engn Dr 2,E1A 07-03, Singapore 117576, Singapore

来源：

CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES | 2020年 / 122卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Data-driven; BP neural network; petal-shaped auxetics; negative Poisson's ratio; structural design; isogeometric analysis; DEEP MATERIAL NETWORK; TOPOLOGY OPTIMIZATION; POISSONS-RATIO; COMPUTATIONAL HOMOGENIZATION; COMPOSITE STRUCTURES; NEURAL NETWORKS; 3D ELASTICITY; BACKPROPAGATION; ARCHITECTURES; MODEL;

D O I：

10.32604/cmes.2020.08680

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：433 / 458

页数：26

共 77 条

[1] Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Akhtar, Naveed
Mian, Ajmal
[J]. IEEE ACCESS, 2018, 6 : 14410 - 14430
[2] Auxetic materials
Alderson, A.
Alderson, K. L.
[J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING, 2007, 221 (G4) : 565 - 575
[3] Artificial neural networks: fundamentals, computing, design, and application
Basheer, IA
Hajmeer, M
[J]. JOURNAL OF MICROBIOLOGICAL METHODS, 2000, 43 (01) : 3 - 31
[4] Isogeometric shell analysis: The Reissner-Mindlin shell
Benson, D. J.
Bazilevs, Y.
Hsu, M. C.
Hughes, T. J. R.
[J]. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2010, 199 (5-8) : 276 - 289
[5] A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality
Bessa, M. A.
Bostanabad, R.
Liu, Z.
Hu, A.
Apley, Daniel W.
Brinson, C.
Chen, W.
Liu, Wing Kam
[J]. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2017, 320 : 633 - 667
[6] Large-Scale Machine Learning with Stochastic Gradient Descent
Bottou, Leon
[J]. COMPSTAT'2010: 19TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STATISTICS, 2010, : 177 - 186
[7] Isogeometric configuration design optimization of shape memory polymer curved beam structures for extremal negative Poisson's ratio
Choi, Myung-Jin
Cho, Seonho
[J]. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2018, 58 (05) : 1861 - 1883
[8] Topology Optimized Architectures with Programmable Poisson's Ratio over Large Deformations
Clausen, Anders
Wang, Fengwen
Jensen, Jakob S.
Sigmund, Ole
Lewis, Jennifer A.
[J]. ADVANCED MATERIALS, 2015, 27 (37) : 5523 - 5527
[9] Collobert R., 2008, P 25 INT C MACH LEAR, P160, DOI DOI 10.1145/1390156.1390177
[10] Collobert R, 2011, J MACH LEARN RES, V12, P2493

← 1 2 3 4 5 6 7 8 →