Please use this identifier to cite or link to this item:
https://doi.org/10.32604/cmes.2020.08680
DC Field | Value | |
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dc.title | Data-driven structural design optimization for petal-shaped auxetics using isogeometric analysis | |
dc.contributor.author | Wang, Y. | |
dc.contributor.author | Liao, Z. | |
dc.contributor.author | Shi, S. | |
dc.contributor.author | Wang, Z. | |
dc.contributor.author | Poh, L.H. | |
dc.date.accessioned | 2021-08-19T04:32:32Z | |
dc.date.available | 2021-08-19T04:32:32Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Wang, Y., Liao, Z., Shi, S., Wang, Z., Poh, L.H. (2020). Data-driven structural design optimization for petal-shaped auxetics using isogeometric analysis. CMES - Computer Modeling in Engineering and Sciences 122 (2) : 433-458. ScholarBank@NUS Repository. https://doi.org/10.32604/cmes.2020.08680 | |
dc.identifier.issn | 1526-1492 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/198050 | |
dc.description.abstract | 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. © 2020 Tech Science Press. All rights reserved. | |
dc.publisher | Tech Science Press | |
dc.source | Scopus OA2020 | |
dc.subject | BP neural network | |
dc.subject | Data-driven | |
dc.subject | Isogeometric analysis | |
dc.subject | Negative Poisson’s ratio | |
dc.subject | Petal-shaped auxetics | |
dc.subject | Structural design | |
dc.type | Article | |
dc.contributor.department | CIVIL AND ENVIRONMENTAL ENGINEERING | |
dc.description.doi | 10.32604/cmes.2020.08680 | |
dc.description.sourcetitle | CMES - Computer Modeling in Engineering and Sciences | |
dc.description.volume | 122 | |
dc.description.issue | 2 | |
dc.description.page | 433-458 | |
dc.published.state | Published | |
Appears in Collections: | Elements Staff Publications |
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